To Know Another’s Pain: A Meta-analysis of Caregivers’ and Healthcare Providers’ Pain Assessment Accuracy

To Know Another’s Pain: A Meta-analysis of Caregivers’ and Healthcare Providers’ Pain... Abstract Background Acute and chronic pain affects millions of adults yet it is often inadequately assessed and treated. Purpose The purpose of the present meta-analysis was to examine the overall level of pain assessment accuracy among caregivers and providers and identify patient, observer, and assessment level factors that moderate pain assessment accuracy. Methods A systematic literature search was conducted in PubMed and PsycINFO to identify studies addressing providers’ pain assessment accuracy, or studies that compared patients’ self-report of pain with observers’ (healthcare providers, caregivers, and strangers) assessment of pain. We present two separate meta-analyses examining the overall effect of under-/overestimation of pain and correlational pain assessment accuracy. Results Seventy-six articles meeting inclusion criteria yielded 94 independent effect sizes for the correlational accuracy meta-analysis. Ninety articles yielded 103 independent effect sizes for the paired comparison meta-analysis. The correlational pain assessment meta-analysis showed that in general, observers were significantly better than chance when assessing pain; however, the paired comparison meta-analysis showed that observers significantly underestimated patients’ pain. Patient’s age and gender, pain type, and provider type moderated these effects. Conclusions Results suggest that certain healthcare providers and caregivers need training to more accurately assess patient pain and that there are particular groups of patients who may be at a greater risk for having their pain inaccurately assessed. Pain assessment, Provider accuracy, Caregiver accuracy, Chronic pain, Acute pain Introduction Pain is a major public health challenge affecting millions of Americans and contributing to morbidity, mortality, disability, healthcare costs, and economic burdens [1]. The majority of acute and chronic pain sufferers have sought medical attention for their pain and chronic pain is one of the most frequent reasons for patients seeking primary care [2, 3]. Despite the number of patients who seek care, chronic pain often goes untreated or undertreated [4–6]. Acute pain is often not recognized and left untreated [7, 8]. In the emergency department, acute pain is often undertreated [8–12] or overmedicated, especially among vulnerable patient groups such as older adults [13, 14]. Untreated pain negatively impacts overall activity, mobility, relationships with others, ability to tolerate treatment, and enjoyment of life [15]. Pain is also associated with multiple comorbid symptoms such as sleep disturbance, fatigue, depression, and anxiety [16, 17]. In addition to the problem of undertreated and untreated pain, overtreatment is also problematic. There is a growing opioid crisis in the USA; the use of prescription opioids has increased substantially over the past decade and is one of the most commonly prescribed groups of medication for controlling pain [14]. Because of health and safety problems with use of opioids and addiction, various healthcare organizations have called for alternative pain relief [1]. In order to provide appropriate levels of pain treatment to avoid both over- and undertreatment of pain, providers and their caregivers must accurately assess patient pain. However, assessment of patient pain can be challenging for physicians and caregivers given that pain is an inherently subjective experience. The International Association for the Study of Pain defines pain as, “an unpleasant sensory and emotional experience that is associated with actual or potential tissue damage or described in such terms” [18, 19]. Pain is a complex experience that requires a multidimensional approach to treatment that takes into account not only the biological but also the social, psychological, and environmental impact on the pain experience and consequently how the experience of pain may impact subsequent health behaviors such as future health seeking [20, 21]. However, according to the Joint Commission on Accreditation of Healthcare Organization’s report on the Core Principles of Pain Assessment and Management, pain is always subjective and a patient’s self-report of pain is the single most reliable indicator of pain [22]. Because of the subjective nature of pain, it is more difficult for observers to understand the experience and respond accordingly compared to other symptoms or diseases that patients may feel and express [23]. There also appears to be an opportunity for improvement around pain assessment, communication, and management given the low levels of satisfaction reported by pain patients [4]. In one qualitative study, chronic pain patients reported feeling misunderstood by their providers, hurried in their appointments, disrespected, treated by providers as though their illness was not real, and labeled by their providers as “drug seekers” [24]. Likewise, physicians have to be aware of deceptive patients who seek medical care in order to obtain pharmacological treatments [25] and physicians and caregivers may become irritated by a patient’s repeated complaints and negative affect associated with pain [23]. As healthcare services increasingly move out of hospitals, the care of patients with pain is often provided with the help of family caregivers. Adequate management of symptoms is accomplished by establishing communication between patients, their caregivers, and an interdisciplinary team of healthcare providers [26]. Two Types of Pain Assessment Accuracy There are a number of ways researchers report accuracy of pain assessment and often multiple accuracy assessments are reported within the same research study. Two of the most common are (a) the correlation between patient-reported pain and observer-judged pain and (b) the paired comparison between patient-reported pain and observer-judged pain. In both cases, the criteria is always a patient’s reported pain as clinicians are encouraged to standardize pain assessment and treat pain assessment as the fifth vital sign [27]. Correlational accuracy Correlational accuracy measures the covariation between patient-reported pain and observer-judged pain and indicates how well patient-reported pain is associated with observer-judged pain. Assessing accuracy in this way does not indicate the direction of inaccuracy (i.e., either over or underestimation). With a correlation coefficient, one does not know how far apart the patient-reported and the observer-judged pain are or which is higher than the other, only whether they covary across pain reports. However, this is frequently how researchers compare patient and observer pain scores. One reason may be that pain ratings from patients and observers do not have to be on the same scale (e.g., some pain rating scales range from 0 to 10 or 0 to 100). A higher correlational accuracy means the observers can discriminate higher intensity levels of pain from lower intensity levels of pain. That is, the correlational approach results in a measure of generality, offering an ability to detect where a patient stands in relation to other patients on a given characteristic such as pain intensity or whether a given patient’s pain has changed over time. Paired comparison accuracy Paired comparison accuracy measures the direction (overestimates or underestimates) and to what degree observers accurately estimate a patient’s pain. The paired comparison method assesses accuracy by calculating the difference between each patient’s pain rating and observer’s pain rating. For this measure, the pain ratings made by patients and observers must be on the same scale and conceptually identical because they are compared directly by subtracting one from the other. Importantly, these two kinds of accuracy are statistically independent of each other and have distinct implications for patient care. A physician who has high correlational pain assessment accuracy may be able to distinguish which patients are in more pain than others. However, a physician may also have low paired comparison accuracy, which would mean they also have a tendency to systematically over or underestimate a patient’s pain. This provider may prescribe relatively correct doses of pain medication, but absolutely the wrong amount for all patients (i.e., no patient gets as much as they need or every patient is over-medicated). We need to look at both correlational and paired comparison accuracy to have a complete understanding of accuracy in patient pain assessment. Moderators of Pain Assessment Accuracy In addition to understanding overall correlational and paired comparison accuracy in patient pain assessment, there are a number of potential moderators of accuracy that may be clinically relevant. If we can identify patient populations or contexts in which patients are more likely to have their pain under or overestimated or less accurately assessed, we can better focus training and pain management interventions. A patient’s characteristics, the assessment method, and the observers have been shown in previous research to potentially moderate pain assessment accuracy. Patient characteristics such as gender and age [28] can impact accuracy. Different types of healthcare providers may vary greatly in their assessment of pain [29] and may differ from caregivers, such as parents and spouses [30, 31]. Characteristics of the pain and the pain assessment method are also important to explore. We know that both chronic and acute pain patients exhibit similar facial expressions of pain [32], but we do not know how this impacts provider or caregiver accuracy. Likewise, pain assessment scales may be highly correlated [33], but it is possible that characteristics of the scale may impact how accurately providers and caregivers assess patient pain. The potential moderators of pain assessment accuracy have not been systematically or quantitatively assessed for these two types of accuracy. Present Research Many individual studies have assessed the accuracy of pain assessments. A recent review qualitatively summarized pain assessment accuracy among healthcare providers [34]. The present study meta-analyzes pain assessment findings to provide an overall, quantitative and systematic evaluation of healthcare providers’ and caregivers’ accuracy in perceiving patient pain. Because the correlational and paired comparison accuracies are conceptually distinct, a meta-analysis across these studies is not possible. Therefore, we present two separate meta-analyses. Methods Definition of Pain Assessment Accuracy For the purpose of this meta-analysis, pain assessment accuracy was defined as the direct comparison between patients’ self-report of pain and observers’ assessment of pain. Pain assessment accuracy was reported in studies as a Pearson correlation coefficient (r), intraclass correlation coefficient (ICC), weighted kappa coefficient, means and standard deviations for the patient and observer, or a difference in the means (d). The patient population included any patients who self-reported pain, including children and older adults with dementia, when a self-report was present. The observer population included any observer including healthcare providers (i.e. physicians, nurses, midwives), informal caregivers (i.e., partners, family members), or other observers who viewed patients in pain. Search Strategy We performed a broad systematic literature search for peer-reviewed articles that contained the terms “pain assessment”, “judgments of pain”, “pain detection”, “pain”, and “pain intensity” combined with terms related to providers, caregivers, and patients (including “provider”, “physician”, “nurse”, “clinician”, “caregiver”, “partner”, “spouse”, and “patient”). The following databases were searched up to March 2016: PubMed (coverage 1946-present) and PsycINFO (coverage 1894-present). The reference lists of relevant studies and systematic reviews were investigated. We also reviewed the reference lists of all articles identified. Articles were included if they were published in peer-reviewed journals and based on patients experiencing some level of pain. Articles were excluded if, (a) they did not directly compare an observer’s judgment of pain with a patient’s verbal or written self-report of pain (the criteria) or if such a comparison could not be calculated based on information provided in the article, (b) observers had access to a patient’s self-reported pain prior to inferring that patient’s pain, or (c) patients were made-up vignettes or scenarios and not actually suffering from pain. Although not an exclusion criteria, no non-English language publications satisfied the inclusion criteria. See Fig. 1 for PRISMA diagram. Fig. 1. View largeDownload slide Flow chart to illustrate the process by which articles were selected or rejected for inclusion in the meta-analyses. Fig. 1. View largeDownload slide Flow chart to illustrate the process by which articles were selected or rejected for inclusion in the meta-analyses. Coding of Potential Moderators A trained research assistant coded all moderators with the first and second author reviewing all data for accuracy and resolving any discrepancies through consensus discussion. Potential moderators coded were number of patients, number of observers, patient age group (children, adults, older adults), patient and observer gender, acute or chronic pain, pain assessment instrument, timing of pain assessment (during or after the pain experience), type of provider and years of clinical experience (for providers only), location of research study, and publication year (coded both as a continuous variable and as a dichotomous variable looking before and after 1996, when the American Pain Society encouraged clinicians to standardize pain assessment and treat pain assessment as the fifth vital sign [27]) (see Table A1 for available moderators in each study). We coded additional potential moderators (e.g., patient and observer race/ethnicity); however, we were unable to include these in our analysis because they were insufficiently reported in the original studies. Effect Size Extraction and Analyses Articles that reported pain assessment accuracy in more than one way (correlation, paired comparison, independent means comparison) are included in more than one meta-analysis. However, within the individual meta-analyses all effect sizes come from independent samples. When multiple pain assessment comparisons were reported for the same group of observers, we averaged across these comparisons to maintain independence of effect sizes. For the correlational meta-analysis, the unit of analysis is the observer. The effect size was calculated using the number of patients if the number of observers was not provided. For the paired comparison meta-analysis, the unit of analysis was the number of ratings or comparisons made or the number of patients in the study. When the patients and observers used different pain scales, we standardized the scales for inclusion in the paired comparison meta-analysis (this only occurred in one study). The effect size used for the correlational meta-analysis was the Pearson correlation coefficient (r) while the standard difference of the means (d) was used for the paired comparison meta-analysis. For studies that used the correlational method, results were already expressed in this metric or the studies’ test statistics were converted to r using standard formulas [35]. All correlational effect sizes were converted using Fisher’s r-to-z transformation (rz) to normalize the data. Effect sizes were averaged to create an unweighted mean effect size across studies. In addition, individual effect sizes were weighted by sample size, averaged to create a weighted mean effect size across studies, and a combined Z was calculated as an indicator of the statistical significance of the set of studies [35]. For significant Zs, a “file drawer N”, an estimate of the number of additional studies with effect sizes averaging r = .00 necessary to bring the combined Z to a nonsignificant level, was calculated. This number takes into account potentially unpublished studies with nonsignificant findings [36]. Cochrane’s Q test for homogeneity was performed to see if effect sizes within the meta-analysis differed from each other more than could be expected due to sampling error. Contrast analyses were computed to examine the impact of moderators on pain assessment accuracy (meta-regression for continuous variables and meta-analysis of variance (ANOVA) for dichotomous variables). Fixed effects comparisons compared different levels of the coded moderators. All analyses were facilitated by Comprehensive Meta-Analysis software [37]. Results Pain Assessment Accuracy: Correlation Seventy-six articles meeting inclusion criteria yielded 94 independent effect sizes for the correlational accuracy meta-analysis (Table 1). The random effects mean effect size was r = .46 (95% CI: 0.41, 0.50) and the fixed, weighted mean effect size was r = .45 (95% CI: 0.44, 0.47) (Table 2). The significant combined Z = 42.27 (p < .001) indicates that observers were significantly better than chance (chance r = 0) at assessing patient pain. It would take 31,427 effect sizes (334.3 missing studies for each study included in the meta-analysis) to nullify the effects. The effect sizes were significantly heterogeneous so moderator analyses were conducted. Table 1 Characteristics and effect sizes of studies included in pain assessment correlational accuracy meta-analysis First author Study year Observer type Observer N Assessment method Correlation effect size (r)§ 95% CI Lower limit 95% CI Upper limit Akin [56] 2013 Caregivers 119 NRS/Likert .76*** 0.67 0.83 2013 Nurses 7 NRS/Likert .40 −0.51 0.89 Allen [30] 2002 Caregivers 176 NRS/Likert .28*** 0.14 0.41 Baxt [31] 2004 Caregivers 276 Faces .43*** 0.33 0.52 Bergh [57] 1999 Nurses 39 VAS .39** 0.08 0.63 Blomqvist [58] 1999 Nurses 29 NRS/Likert .63*** 0.35 0.81 Broberger [59] 2005 Caregivers 54 VAS .50*** 0.27 0.68 2005 Nurses 29 VAS .41* 0.05 0.68 Chambers [60] 1998 Caregivers 110 Faces .72*** 0.62 0.80 Choiniere [61] 1990 Nurses 82 VAS .47*** 0.28 0.62 Clipp [62] 1992 Caregivers 30 Other .46** 0.12 0.70 Colwell [63] 1996 Nurses 44 NRS/Likert .70*** 0.51 0.83 Cremeans-Smith [64] 2003 Caregivers 114 NRS/Likert .42*** 0.26 0.56 2003 Physicians 5 NRS/Likert .08 −0.86 0.90 Dawber [65] 2016 Caregivers 50 Other .58*** 0.36 0.74 2016 Nurses 50 Other .31* 0.03 0.54 de Bock [66] 1994 Physicians 40 VAS .36* 0.05 0.60 Doherty [67] 1993 Caregivers 20 VAS .45* 0.01 0.74 Drayer [68] 1999 Other healthcare 45 VAS .36* 0.07 0.59 Everett [69] 1994 Nurses 27 VAS .70*** 0.44 0.85 Forrest [70] 1989 Physicians 8 VAS .64 −0.12 0.93 Fridh [71] 1990 Nurses 12 VAS .29 −0.34 0.74 Gil [72] 2004 Caregivers 35 NRS/Likert .50** 0.20 0.71 Harrison [73] 1993 Nurses 199 VAS .34*** 0.22 0.46 Hays [74] 1995 Caregivers 304 NRS/Likert .56*** 0.48 0.63 Heikkinen [75] 2005 Nurses 45 Other .85*** 0.75 0.92 Heuss [76] 2012 Nurses 12 VAS .35 −0.28 0.77 2012 Physicians 9 VAS .37 −0.39 0.83 Higginson [77] 2008 Caregivers 64 VAS .69 0.53 0.80 Hodgkins [78] 1985 Physicians 21 VAS .62** 0.26 0.83 Horgas [79] 2001 Nurses 16 Other −.25 −0.66 0.28 Idvall [80] 2005 Nurses 267 NRS/Likert .58*** 0.49 0.65 Kelly [81] 2002 Caregivers 78 VAS .63*** 0.47 0.75 Kristjanson [82] 1998 Caregivers 78 Other .44*** 0.24 0.60 Lautenbacher [28] 2013 Nurses 21 NRS/Likert .10*** −0.35 0.51 2013 Other observers 21 NRS/Likert .07 −0.37 0.49 Lin [83] 2001 Caregivers 89 NRS/Likert .58*** 0.42 0.70 Lobchuk [84] 1997 Caregivers 37 Other .31 −0.02 0.58 Lobchuk [20] 2002 Caregivers 98 NRS/Likert .69*** 0.57 0.78 Madison [85] 1995 Caregivers 18 VAS .49* 0.03 0.78 Maguire [86] 2014 Physicians 200 VAS .16* 0.02 0.29 Manne [87] 1992 Caregivers 85 Faces .32** 0.11 0.50 1992 Nurses 9 Faces .63 −0.06 0.91 Mantyselka [5] 2001 Physicians 28 NRS/Likert .29 −0.09 0.60 McKinley [88] 1991 Nurses 97 VAS .35*** 0.16 0.51 McMillan [89] 2003 Caregivers 264 NRS/Likert .40*** 0.29 0.50 McPherson [90] 2008 Caregivers 66 NRS/Likert .44*** 0.22 0.62 Miller [91] 1996 Caregivers 20 VAS .65*** 0.29 0.85 1996 Nurses 20 VAS .39 −0.06 0.71 O’Brien [92] 1988 Caregivers 42 VAS .23 −0.08 0.50 Oi-Ling [93] 2005 Caregivers 30 NRS/Likert .38* 0.02 0.65 Paice [94] 1991 Nurses 30 NRS/Likert .06 −0.31 0.41 1991 Physicians 30 NRS/Likert .12 −0.25 0.46 Perreault [95] 2006 Other healthcare 9 NRS/Likert .55 −0.18 0.89 Powers [96] 1987 Nurses 33 VAS .32 −0.03 0.60 Prkachin [97] 1994 Other observers 5 NRS/Likert .37 −0.76 0.94 Prkachin [98] 2001 Other observers 82 NRS/Likert .36*** 0.16 0.54 2001 Physicians 34 Other .36* 0.02 0.62 Redinbaugh [99] 2002 Caregivers 31 NRS/Likert .55*** 0.24 0.76 Resnizky [100] 2006 Caregivers 143 Other .51*** 0.38 0.62 Rhondali [101] 2012 Nurses 20 NRS/Likert .66*** 0.31 0.85 Ruben [7] 2013 Other observers 262 VAS .10 −0.02 0.22 Ruben [102] 2016 Other observers 95 NRS/Likert .12 −0.08 0.31 Salmon [103] 1996 Nurses 15 VAS .62** 0.16 0.86 Schneider [104] 1992 Caregivers 40 Other .72*** 0.53 0.84 1992 Nurses 40 Other .60*** 0.35 0.77 Shega [105] 2004 Caregivers 115 Other .12 −0.06 0.30 Silveira [106] 2010 Caregivers 142 NRS/Likert .48*** 0.34 0.60 Singer [107] 1999 Physicians 1171 Faces .47*** 0.42 0.51 Singer [108] 2002 Caregivers 57 Faces .47*** 0.24 0.65 2002 Other healthcare 57 VAS .08 −0.18 0.33 Sjöström [109] 1997 Nurses 90 VAS .55*** 0.39 0.68 1997 Physicians 90 VAS .68*** 0.55 0.78 Sneeuw [110] 1997 Caregivers 103 NRS/Likert .23* 0.04 0.41 Sneeuw [111] 1998 Caregivers 307 VAS .63*** 0.56 0.69 Sneeuw [112] 1999 Caregivers 90 Faces .64*** 0.50 0.75 1999 Nurses 35 Faces .66*** 0.42 0.81 1999 Physicians 15 Faces .50 −0.02 0.81 Stephenson [113] 1994 Nurses 11 VAS .59 −0.02 0.88 St-Laurent-Gagnon [114] 1999 Caregivers 104 Faces .76*** 0.67 0.83 Suarez-Almazor [115] 2001 Physicians 5 VAS .42 −0.73 0.95 Sutherland [116] 1988 Physicians 22 VAS .66*** 0.33 0.85 van Herk [117] 2009 Caregivers 122 NRS/Likert .28** 0.11 0.44 2009 Nurses 171 NRS/Likert .20** 0.05 0.34 Van Der Does [118] 1989 Nurses 30 VAS .39* 0.03 0.66 Vervoort [119] 2009 Caregivers 62 NRS/Likert .16 −0.09 0.39 Walkenstein [120] 1982 Nurses 8 Other .44 −0.38 0.87 Weiner [121] 1999 Caregivers 42 NRS/Likert −.19 −0.47 0.12 1999 Nurses 42 NRS/Likert .34* 0.04 0.58 Wennman-Larsen [122] 2007 Caregivers 54 NRS/Likert .64*** 0.45 0.77 Yesilbalkan [123] 2010 Caregivers 80 NRS/Likert .40*** 0.20 0.57 Zalon [124] 1993 Nurses 119 VAS .30*** 0.13 0.46 Zhukovsky [125] 2015 Caregivers 60 Other .40*** 0.16 0.59 2015 Physicians 14 Other .70** 0.27 0.90 First author Study year Observer type Observer N Assessment method Correlation effect size (r)§ 95% CI Lower limit 95% CI Upper limit Akin [56] 2013 Caregivers 119 NRS/Likert .76*** 0.67 0.83 2013 Nurses 7 NRS/Likert .40 −0.51 0.89 Allen [30] 2002 Caregivers 176 NRS/Likert .28*** 0.14 0.41 Baxt [31] 2004 Caregivers 276 Faces .43*** 0.33 0.52 Bergh [57] 1999 Nurses 39 VAS .39** 0.08 0.63 Blomqvist [58] 1999 Nurses 29 NRS/Likert .63*** 0.35 0.81 Broberger [59] 2005 Caregivers 54 VAS .50*** 0.27 0.68 2005 Nurses 29 VAS .41* 0.05 0.68 Chambers [60] 1998 Caregivers 110 Faces .72*** 0.62 0.80 Choiniere [61] 1990 Nurses 82 VAS .47*** 0.28 0.62 Clipp [62] 1992 Caregivers 30 Other .46** 0.12 0.70 Colwell [63] 1996 Nurses 44 NRS/Likert .70*** 0.51 0.83 Cremeans-Smith [64] 2003 Caregivers 114 NRS/Likert .42*** 0.26 0.56 2003 Physicians 5 NRS/Likert .08 −0.86 0.90 Dawber [65] 2016 Caregivers 50 Other .58*** 0.36 0.74 2016 Nurses 50 Other .31* 0.03 0.54 de Bock [66] 1994 Physicians 40 VAS .36* 0.05 0.60 Doherty [67] 1993 Caregivers 20 VAS .45* 0.01 0.74 Drayer [68] 1999 Other healthcare 45 VAS .36* 0.07 0.59 Everett [69] 1994 Nurses 27 VAS .70*** 0.44 0.85 Forrest [70] 1989 Physicians 8 VAS .64 −0.12 0.93 Fridh [71] 1990 Nurses 12 VAS .29 −0.34 0.74 Gil [72] 2004 Caregivers 35 NRS/Likert .50** 0.20 0.71 Harrison [73] 1993 Nurses 199 VAS .34*** 0.22 0.46 Hays [74] 1995 Caregivers 304 NRS/Likert .56*** 0.48 0.63 Heikkinen [75] 2005 Nurses 45 Other .85*** 0.75 0.92 Heuss [76] 2012 Nurses 12 VAS .35 −0.28 0.77 2012 Physicians 9 VAS .37 −0.39 0.83 Higginson [77] 2008 Caregivers 64 VAS .69 0.53 0.80 Hodgkins [78] 1985 Physicians 21 VAS .62** 0.26 0.83 Horgas [79] 2001 Nurses 16 Other −.25 −0.66 0.28 Idvall [80] 2005 Nurses 267 NRS/Likert .58*** 0.49 0.65 Kelly [81] 2002 Caregivers 78 VAS .63*** 0.47 0.75 Kristjanson [82] 1998 Caregivers 78 Other .44*** 0.24 0.60 Lautenbacher [28] 2013 Nurses 21 NRS/Likert .10*** −0.35 0.51 2013 Other observers 21 NRS/Likert .07 −0.37 0.49 Lin [83] 2001 Caregivers 89 NRS/Likert .58*** 0.42 0.70 Lobchuk [84] 1997 Caregivers 37 Other .31 −0.02 0.58 Lobchuk [20] 2002 Caregivers 98 NRS/Likert .69*** 0.57 0.78 Madison [85] 1995 Caregivers 18 VAS .49* 0.03 0.78 Maguire [86] 2014 Physicians 200 VAS .16* 0.02 0.29 Manne [87] 1992 Caregivers 85 Faces .32** 0.11 0.50 1992 Nurses 9 Faces .63 −0.06 0.91 Mantyselka [5] 2001 Physicians 28 NRS/Likert .29 −0.09 0.60 McKinley [88] 1991 Nurses 97 VAS .35*** 0.16 0.51 McMillan [89] 2003 Caregivers 264 NRS/Likert .40*** 0.29 0.50 McPherson [90] 2008 Caregivers 66 NRS/Likert .44*** 0.22 0.62 Miller [91] 1996 Caregivers 20 VAS .65*** 0.29 0.85 1996 Nurses 20 VAS .39 −0.06 0.71 O’Brien [92] 1988 Caregivers 42 VAS .23 −0.08 0.50 Oi-Ling [93] 2005 Caregivers 30 NRS/Likert .38* 0.02 0.65 Paice [94] 1991 Nurses 30 NRS/Likert .06 −0.31 0.41 1991 Physicians 30 NRS/Likert .12 −0.25 0.46 Perreault [95] 2006 Other healthcare 9 NRS/Likert .55 −0.18 0.89 Powers [96] 1987 Nurses 33 VAS .32 −0.03 0.60 Prkachin [97] 1994 Other observers 5 NRS/Likert .37 −0.76 0.94 Prkachin [98] 2001 Other observers 82 NRS/Likert .36*** 0.16 0.54 2001 Physicians 34 Other .36* 0.02 0.62 Redinbaugh [99] 2002 Caregivers 31 NRS/Likert .55*** 0.24 0.76 Resnizky [100] 2006 Caregivers 143 Other .51*** 0.38 0.62 Rhondali [101] 2012 Nurses 20 NRS/Likert .66*** 0.31 0.85 Ruben [7] 2013 Other observers 262 VAS .10 −0.02 0.22 Ruben [102] 2016 Other observers 95 NRS/Likert .12 −0.08 0.31 Salmon [103] 1996 Nurses 15 VAS .62** 0.16 0.86 Schneider [104] 1992 Caregivers 40 Other .72*** 0.53 0.84 1992 Nurses 40 Other .60*** 0.35 0.77 Shega [105] 2004 Caregivers 115 Other .12 −0.06 0.30 Silveira [106] 2010 Caregivers 142 NRS/Likert .48*** 0.34 0.60 Singer [107] 1999 Physicians 1171 Faces .47*** 0.42 0.51 Singer [108] 2002 Caregivers 57 Faces .47*** 0.24 0.65 2002 Other healthcare 57 VAS .08 −0.18 0.33 Sjöström [109] 1997 Nurses 90 VAS .55*** 0.39 0.68 1997 Physicians 90 VAS .68*** 0.55 0.78 Sneeuw [110] 1997 Caregivers 103 NRS/Likert .23* 0.04 0.41 Sneeuw [111] 1998 Caregivers 307 VAS .63*** 0.56 0.69 Sneeuw [112] 1999 Caregivers 90 Faces .64*** 0.50 0.75 1999 Nurses 35 Faces .66*** 0.42 0.81 1999 Physicians 15 Faces .50 −0.02 0.81 Stephenson [113] 1994 Nurses 11 VAS .59 −0.02 0.88 St-Laurent-Gagnon [114] 1999 Caregivers 104 Faces .76*** 0.67 0.83 Suarez-Almazor [115] 2001 Physicians 5 VAS .42 −0.73 0.95 Sutherland [116] 1988 Physicians 22 VAS .66*** 0.33 0.85 van Herk [117] 2009 Caregivers 122 NRS/Likert .28** 0.11 0.44 2009 Nurses 171 NRS/Likert .20** 0.05 0.34 Van Der Does [118] 1989 Nurses 30 VAS .39* 0.03 0.66 Vervoort [119] 2009 Caregivers 62 NRS/Likert .16 −0.09 0.39 Walkenstein [120] 1982 Nurses 8 Other .44 −0.38 0.87 Weiner [121] 1999 Caregivers 42 NRS/Likert −.19 −0.47 0.12 1999 Nurses 42 NRS/Likert .34* 0.04 0.58 Wennman-Larsen [122] 2007 Caregivers 54 NRS/Likert .64*** 0.45 0.77 Yesilbalkan [123] 2010 Caregivers 80 NRS/Likert .40*** 0.20 0.57 Zalon [124] 1993 Nurses 119 VAS .30*** 0.13 0.46 Zhukovsky [125] 2015 Caregivers 60 Other .40*** 0.16 0.59 2015 Physicians 14 Other .70** 0.27 0.90 CI confidence interval; NRS numeric rating scale; VAS visual analogue scale. Faces is Faces Pain Scale. §t-statistic converted to r using formula r = t2(t2+df) *p < .05; **p < .01; ***p < .001. View Large Table 1 Characteristics and effect sizes of studies included in pain assessment correlational accuracy meta-analysis First author Study year Observer type Observer N Assessment method Correlation effect size (r)§ 95% CI Lower limit 95% CI Upper limit Akin [56] 2013 Caregivers 119 NRS/Likert .76*** 0.67 0.83 2013 Nurses 7 NRS/Likert .40 −0.51 0.89 Allen [30] 2002 Caregivers 176 NRS/Likert .28*** 0.14 0.41 Baxt [31] 2004 Caregivers 276 Faces .43*** 0.33 0.52 Bergh [57] 1999 Nurses 39 VAS .39** 0.08 0.63 Blomqvist [58] 1999 Nurses 29 NRS/Likert .63*** 0.35 0.81 Broberger [59] 2005 Caregivers 54 VAS .50*** 0.27 0.68 2005 Nurses 29 VAS .41* 0.05 0.68 Chambers [60] 1998 Caregivers 110 Faces .72*** 0.62 0.80 Choiniere [61] 1990 Nurses 82 VAS .47*** 0.28 0.62 Clipp [62] 1992 Caregivers 30 Other .46** 0.12 0.70 Colwell [63] 1996 Nurses 44 NRS/Likert .70*** 0.51 0.83 Cremeans-Smith [64] 2003 Caregivers 114 NRS/Likert .42*** 0.26 0.56 2003 Physicians 5 NRS/Likert .08 −0.86 0.90 Dawber [65] 2016 Caregivers 50 Other .58*** 0.36 0.74 2016 Nurses 50 Other .31* 0.03 0.54 de Bock [66] 1994 Physicians 40 VAS .36* 0.05 0.60 Doherty [67] 1993 Caregivers 20 VAS .45* 0.01 0.74 Drayer [68] 1999 Other healthcare 45 VAS .36* 0.07 0.59 Everett [69] 1994 Nurses 27 VAS .70*** 0.44 0.85 Forrest [70] 1989 Physicians 8 VAS .64 −0.12 0.93 Fridh [71] 1990 Nurses 12 VAS .29 −0.34 0.74 Gil [72] 2004 Caregivers 35 NRS/Likert .50** 0.20 0.71 Harrison [73] 1993 Nurses 199 VAS .34*** 0.22 0.46 Hays [74] 1995 Caregivers 304 NRS/Likert .56*** 0.48 0.63 Heikkinen [75] 2005 Nurses 45 Other .85*** 0.75 0.92 Heuss [76] 2012 Nurses 12 VAS .35 −0.28 0.77 2012 Physicians 9 VAS .37 −0.39 0.83 Higginson [77] 2008 Caregivers 64 VAS .69 0.53 0.80 Hodgkins [78] 1985 Physicians 21 VAS .62** 0.26 0.83 Horgas [79] 2001 Nurses 16 Other −.25 −0.66 0.28 Idvall [80] 2005 Nurses 267 NRS/Likert .58*** 0.49 0.65 Kelly [81] 2002 Caregivers 78 VAS .63*** 0.47 0.75 Kristjanson [82] 1998 Caregivers 78 Other .44*** 0.24 0.60 Lautenbacher [28] 2013 Nurses 21 NRS/Likert .10*** −0.35 0.51 2013 Other observers 21 NRS/Likert .07 −0.37 0.49 Lin [83] 2001 Caregivers 89 NRS/Likert .58*** 0.42 0.70 Lobchuk [84] 1997 Caregivers 37 Other .31 −0.02 0.58 Lobchuk [20] 2002 Caregivers 98 NRS/Likert .69*** 0.57 0.78 Madison [85] 1995 Caregivers 18 VAS .49* 0.03 0.78 Maguire [86] 2014 Physicians 200 VAS .16* 0.02 0.29 Manne [87] 1992 Caregivers 85 Faces .32** 0.11 0.50 1992 Nurses 9 Faces .63 −0.06 0.91 Mantyselka [5] 2001 Physicians 28 NRS/Likert .29 −0.09 0.60 McKinley [88] 1991 Nurses 97 VAS .35*** 0.16 0.51 McMillan [89] 2003 Caregivers 264 NRS/Likert .40*** 0.29 0.50 McPherson [90] 2008 Caregivers 66 NRS/Likert .44*** 0.22 0.62 Miller [91] 1996 Caregivers 20 VAS .65*** 0.29 0.85 1996 Nurses 20 VAS .39 −0.06 0.71 O’Brien [92] 1988 Caregivers 42 VAS .23 −0.08 0.50 Oi-Ling [93] 2005 Caregivers 30 NRS/Likert .38* 0.02 0.65 Paice [94] 1991 Nurses 30 NRS/Likert .06 −0.31 0.41 1991 Physicians 30 NRS/Likert .12 −0.25 0.46 Perreault [95] 2006 Other healthcare 9 NRS/Likert .55 −0.18 0.89 Powers [96] 1987 Nurses 33 VAS .32 −0.03 0.60 Prkachin [97] 1994 Other observers 5 NRS/Likert .37 −0.76 0.94 Prkachin [98] 2001 Other observers 82 NRS/Likert .36*** 0.16 0.54 2001 Physicians 34 Other .36* 0.02 0.62 Redinbaugh [99] 2002 Caregivers 31 NRS/Likert .55*** 0.24 0.76 Resnizky [100] 2006 Caregivers 143 Other .51*** 0.38 0.62 Rhondali [101] 2012 Nurses 20 NRS/Likert .66*** 0.31 0.85 Ruben [7] 2013 Other observers 262 VAS .10 −0.02 0.22 Ruben [102] 2016 Other observers 95 NRS/Likert .12 −0.08 0.31 Salmon [103] 1996 Nurses 15 VAS .62** 0.16 0.86 Schneider [104] 1992 Caregivers 40 Other .72*** 0.53 0.84 1992 Nurses 40 Other .60*** 0.35 0.77 Shega [105] 2004 Caregivers 115 Other .12 −0.06 0.30 Silveira [106] 2010 Caregivers 142 NRS/Likert .48*** 0.34 0.60 Singer [107] 1999 Physicians 1171 Faces .47*** 0.42 0.51 Singer [108] 2002 Caregivers 57 Faces .47*** 0.24 0.65 2002 Other healthcare 57 VAS .08 −0.18 0.33 Sjöström [109] 1997 Nurses 90 VAS .55*** 0.39 0.68 1997 Physicians 90 VAS .68*** 0.55 0.78 Sneeuw [110] 1997 Caregivers 103 NRS/Likert .23* 0.04 0.41 Sneeuw [111] 1998 Caregivers 307 VAS .63*** 0.56 0.69 Sneeuw [112] 1999 Caregivers 90 Faces .64*** 0.50 0.75 1999 Nurses 35 Faces .66*** 0.42 0.81 1999 Physicians 15 Faces .50 −0.02 0.81 Stephenson [113] 1994 Nurses 11 VAS .59 −0.02 0.88 St-Laurent-Gagnon [114] 1999 Caregivers 104 Faces .76*** 0.67 0.83 Suarez-Almazor [115] 2001 Physicians 5 VAS .42 −0.73 0.95 Sutherland [116] 1988 Physicians 22 VAS .66*** 0.33 0.85 van Herk [117] 2009 Caregivers 122 NRS/Likert .28** 0.11 0.44 2009 Nurses 171 NRS/Likert .20** 0.05 0.34 Van Der Does [118] 1989 Nurses 30 VAS .39* 0.03 0.66 Vervoort [119] 2009 Caregivers 62 NRS/Likert .16 −0.09 0.39 Walkenstein [120] 1982 Nurses 8 Other .44 −0.38 0.87 Weiner [121] 1999 Caregivers 42 NRS/Likert −.19 −0.47 0.12 1999 Nurses 42 NRS/Likert .34* 0.04 0.58 Wennman-Larsen [122] 2007 Caregivers 54 NRS/Likert .64*** 0.45 0.77 Yesilbalkan [123] 2010 Caregivers 80 NRS/Likert .40*** 0.20 0.57 Zalon [124] 1993 Nurses 119 VAS .30*** 0.13 0.46 Zhukovsky [125] 2015 Caregivers 60 Other .40*** 0.16 0.59 2015 Physicians 14 Other .70** 0.27 0.90 First author Study year Observer type Observer N Assessment method Correlation effect size (r)§ 95% CI Lower limit 95% CI Upper limit Akin [56] 2013 Caregivers 119 NRS/Likert .76*** 0.67 0.83 2013 Nurses 7 NRS/Likert .40 −0.51 0.89 Allen [30] 2002 Caregivers 176 NRS/Likert .28*** 0.14 0.41 Baxt [31] 2004 Caregivers 276 Faces .43*** 0.33 0.52 Bergh [57] 1999 Nurses 39 VAS .39** 0.08 0.63 Blomqvist [58] 1999 Nurses 29 NRS/Likert .63*** 0.35 0.81 Broberger [59] 2005 Caregivers 54 VAS .50*** 0.27 0.68 2005 Nurses 29 VAS .41* 0.05 0.68 Chambers [60] 1998 Caregivers 110 Faces .72*** 0.62 0.80 Choiniere [61] 1990 Nurses 82 VAS .47*** 0.28 0.62 Clipp [62] 1992 Caregivers 30 Other .46** 0.12 0.70 Colwell [63] 1996 Nurses 44 NRS/Likert .70*** 0.51 0.83 Cremeans-Smith [64] 2003 Caregivers 114 NRS/Likert .42*** 0.26 0.56 2003 Physicians 5 NRS/Likert .08 −0.86 0.90 Dawber [65] 2016 Caregivers 50 Other .58*** 0.36 0.74 2016 Nurses 50 Other .31* 0.03 0.54 de Bock [66] 1994 Physicians 40 VAS .36* 0.05 0.60 Doherty [67] 1993 Caregivers 20 VAS .45* 0.01 0.74 Drayer [68] 1999 Other healthcare 45 VAS .36* 0.07 0.59 Everett [69] 1994 Nurses 27 VAS .70*** 0.44 0.85 Forrest [70] 1989 Physicians 8 VAS .64 −0.12 0.93 Fridh [71] 1990 Nurses 12 VAS .29 −0.34 0.74 Gil [72] 2004 Caregivers 35 NRS/Likert .50** 0.20 0.71 Harrison [73] 1993 Nurses 199 VAS .34*** 0.22 0.46 Hays [74] 1995 Caregivers 304 NRS/Likert .56*** 0.48 0.63 Heikkinen [75] 2005 Nurses 45 Other .85*** 0.75 0.92 Heuss [76] 2012 Nurses 12 VAS .35 −0.28 0.77 2012 Physicians 9 VAS .37 −0.39 0.83 Higginson [77] 2008 Caregivers 64 VAS .69 0.53 0.80 Hodgkins [78] 1985 Physicians 21 VAS .62** 0.26 0.83 Horgas [79] 2001 Nurses 16 Other −.25 −0.66 0.28 Idvall [80] 2005 Nurses 267 NRS/Likert .58*** 0.49 0.65 Kelly [81] 2002 Caregivers 78 VAS .63*** 0.47 0.75 Kristjanson [82] 1998 Caregivers 78 Other .44*** 0.24 0.60 Lautenbacher [28] 2013 Nurses 21 NRS/Likert .10*** −0.35 0.51 2013 Other observers 21 NRS/Likert .07 −0.37 0.49 Lin [83] 2001 Caregivers 89 NRS/Likert .58*** 0.42 0.70 Lobchuk [84] 1997 Caregivers 37 Other .31 −0.02 0.58 Lobchuk [20] 2002 Caregivers 98 NRS/Likert .69*** 0.57 0.78 Madison [85] 1995 Caregivers 18 VAS .49* 0.03 0.78 Maguire [86] 2014 Physicians 200 VAS .16* 0.02 0.29 Manne [87] 1992 Caregivers 85 Faces .32** 0.11 0.50 1992 Nurses 9 Faces .63 −0.06 0.91 Mantyselka [5] 2001 Physicians 28 NRS/Likert .29 −0.09 0.60 McKinley [88] 1991 Nurses 97 VAS .35*** 0.16 0.51 McMillan [89] 2003 Caregivers 264 NRS/Likert .40*** 0.29 0.50 McPherson [90] 2008 Caregivers 66 NRS/Likert .44*** 0.22 0.62 Miller [91] 1996 Caregivers 20 VAS .65*** 0.29 0.85 1996 Nurses 20 VAS .39 −0.06 0.71 O’Brien [92] 1988 Caregivers 42 VAS .23 −0.08 0.50 Oi-Ling [93] 2005 Caregivers 30 NRS/Likert .38* 0.02 0.65 Paice [94] 1991 Nurses 30 NRS/Likert .06 −0.31 0.41 1991 Physicians 30 NRS/Likert .12 −0.25 0.46 Perreault [95] 2006 Other healthcare 9 NRS/Likert .55 −0.18 0.89 Powers [96] 1987 Nurses 33 VAS .32 −0.03 0.60 Prkachin [97] 1994 Other observers 5 NRS/Likert .37 −0.76 0.94 Prkachin [98] 2001 Other observers 82 NRS/Likert .36*** 0.16 0.54 2001 Physicians 34 Other .36* 0.02 0.62 Redinbaugh [99] 2002 Caregivers 31 NRS/Likert .55*** 0.24 0.76 Resnizky [100] 2006 Caregivers 143 Other .51*** 0.38 0.62 Rhondali [101] 2012 Nurses 20 NRS/Likert .66*** 0.31 0.85 Ruben [7] 2013 Other observers 262 VAS .10 −0.02 0.22 Ruben [102] 2016 Other observers 95 NRS/Likert .12 −0.08 0.31 Salmon [103] 1996 Nurses 15 VAS .62** 0.16 0.86 Schneider [104] 1992 Caregivers 40 Other .72*** 0.53 0.84 1992 Nurses 40 Other .60*** 0.35 0.77 Shega [105] 2004 Caregivers 115 Other .12 −0.06 0.30 Silveira [106] 2010 Caregivers 142 NRS/Likert .48*** 0.34 0.60 Singer [107] 1999 Physicians 1171 Faces .47*** 0.42 0.51 Singer [108] 2002 Caregivers 57 Faces .47*** 0.24 0.65 2002 Other healthcare 57 VAS .08 −0.18 0.33 Sjöström [109] 1997 Nurses 90 VAS .55*** 0.39 0.68 1997 Physicians 90 VAS .68*** 0.55 0.78 Sneeuw [110] 1997 Caregivers 103 NRS/Likert .23* 0.04 0.41 Sneeuw [111] 1998 Caregivers 307 VAS .63*** 0.56 0.69 Sneeuw [112] 1999 Caregivers 90 Faces .64*** 0.50 0.75 1999 Nurses 35 Faces .66*** 0.42 0.81 1999 Physicians 15 Faces .50 −0.02 0.81 Stephenson [113] 1994 Nurses 11 VAS .59 −0.02 0.88 St-Laurent-Gagnon [114] 1999 Caregivers 104 Faces .76*** 0.67 0.83 Suarez-Almazor [115] 2001 Physicians 5 VAS .42 −0.73 0.95 Sutherland [116] 1988 Physicians 22 VAS .66*** 0.33 0.85 van Herk [117] 2009 Caregivers 122 NRS/Likert .28** 0.11 0.44 2009 Nurses 171 NRS/Likert .20** 0.05 0.34 Van Der Does [118] 1989 Nurses 30 VAS .39* 0.03 0.66 Vervoort [119] 2009 Caregivers 62 NRS/Likert .16 −0.09 0.39 Walkenstein [120] 1982 Nurses 8 Other .44 −0.38 0.87 Weiner [121] 1999 Caregivers 42 NRS/Likert −.19 −0.47 0.12 1999 Nurses 42 NRS/Likert .34* 0.04 0.58 Wennman-Larsen [122] 2007 Caregivers 54 NRS/Likert .64*** 0.45 0.77 Yesilbalkan [123] 2010 Caregivers 80 NRS/Likert .40*** 0.20 0.57 Zalon [124] 1993 Nurses 119 VAS .30*** 0.13 0.46 Zhukovsky [125] 2015 Caregivers 60 Other .40*** 0.16 0.59 2015 Physicians 14 Other .70** 0.27 0.90 CI confidence interval; NRS numeric rating scale; VAS visual analogue scale. Faces is Faces Pain Scale. §t-statistic converted to r using formula r = t2(t2+df) *p < .05; **p < .01; ***p < .001. View Large Table 2 Overall effect size summary for pain assessment accuracy reported as correlations and paired comparisons Effect sizes N Range of effect sizes Fixed effect size (95% CI) Random effect size (95% CI) Combined Z Test of homogeneity Correlation 94 −.25–.85 0.45 (0.44, 0.47) 0.46 (0.41, 0.50) 42.27*** 416.52*** Paired Comparison 103 −4.66–.88 −0.13 (−0.15, −0.10) −0.26 (−0.35, −0.17) −10.88*** 1,286.90*** Effect sizes N Range of effect sizes Fixed effect size (95% CI) Random effect size (95% CI) Combined Z Test of homogeneity Correlation 94 −.25–.85 0.45 (0.44, 0.47) 0.46 (0.41, 0.50) 42.27*** 416.52*** Paired Comparison 103 −4.66–.88 −0.13 (−0.15, −0.10) −0.26 (−0.35, −0.17) −10.88*** 1,286.90*** ***p < .001. Correlation effect sizes are r and paired comparison effect sizes are d. View Large Table 2 Overall effect size summary for pain assessment accuracy reported as correlations and paired comparisons Effect sizes N Range of effect sizes Fixed effect size (95% CI) Random effect size (95% CI) Combined Z Test of homogeneity Correlation 94 −.25–.85 0.45 (0.44, 0.47) 0.46 (0.41, 0.50) 42.27*** 416.52*** Paired Comparison 103 −4.66–.88 −0.13 (−0.15, −0.10) −0.26 (−0.35, −0.17) −10.88*** 1,286.90*** Effect sizes N Range of effect sizes Fixed effect size (95% CI) Random effect size (95% CI) Combined Z Test of homogeneity Correlation 94 −.25–.85 0.45 (0.44, 0.47) 0.46 (0.41, 0.50) 42.27*** 416.52*** Paired Comparison 103 −4.66–.88 −0.13 (−0.15, −0.10) −0.26 (−0.35, −0.17) −10.88*** 1,286.90*** ***p < .001. Correlation effect sizes are r and paired comparison effect sizes are d. View Large Characteristics of the study: correlation Studies were published from 1982 to 2016. Study year was not significant as a continuous moderator (β = −.00, 95% CI: −0.01, 0.00) or in a dichotomous analysis examining differences in the effect size before or after 1996 (Table 3). The majority of studies were conducted in the USA or Canada (54%) or in Europe (34%). Studies conducted in the USA/Canada had significantly lower accuracy than studies conducted in Europe or Asia (Q = 17.17, p < .001). Table 3 Moderators of correlational pain assessment accuracy Moderator Categories Effect size (r) Number of effect sizes Fixed effects comparisons Year <1996 .45 30 0.00 ≥1996 .45 64 Country Asia/Middle East .51 8 17.17*** Europe .51 28 USA/Canada .43 54 Other .41 4 Patient Gender <50% male .46 33 1.60 ≥50% male .44 44 Patient Age Group Children .52 18 9.32* Adults .44 53 Older adults .45 7 Mixed .44 16 Observer type Informal caregivers .50 40 69.79*** Nurses .45 31 Physicians .45 15 Other healthcare providers .23 3 Other observers .15 5 Observer Gender <50% male .47 52 4.13* ≥50% male .54 5 Type of Pain Acute .45 39 2.72 Chronic .47 47 Mixed .35 4 Pain Condition Arthritis/musculoskeletal pain .28 12 110.69*** Burn .55 3 Cancer .51 35 Surgery or procedure .53 27 Laboratory .11 5 Assessment Method Faces Pain Scale .54 10 11.58** NRS/Likert .44 35 VAS .44 34 Other, mixed .46 14 Location of assessment Inpatient .49 32 77.22*** Outpatient .43 40 Laboratory .11 3 Mixed .54 19 Moderator Categories Effect size (r) Number of effect sizes Fixed effects comparisons Year <1996 .45 30 0.00 ≥1996 .45 64 Country Asia/Middle East .51 8 17.17*** Europe .51 28 USA/Canada .43 54 Other .41 4 Patient Gender <50% male .46 33 1.60 ≥50% male .44 44 Patient Age Group Children .52 18 9.32* Adults .44 53 Older adults .45 7 Mixed .44 16 Observer type Informal caregivers .50 40 69.79*** Nurses .45 31 Physicians .45 15 Other healthcare providers .23 3 Other observers .15 5 Observer Gender <50% male .47 52 4.13* ≥50% male .54 5 Type of Pain Acute .45 39 2.72 Chronic .47 47 Mixed .35 4 Pain Condition Arthritis/musculoskeletal pain .28 12 110.69*** Burn .55 3 Cancer .51 35 Surgery or procedure .53 27 Laboratory .11 5 Assessment Method Faces Pain Scale .54 10 11.58** NRS/Likert .44 35 VAS .44 34 Other, mixed .46 14 Location of assessment Inpatient .49 32 77.22*** Outpatient .43 40 Laboratory .11 3 Mixed .54 19 *p < .05; **p < .01; ***p < .001. NRS numeric rating scale; VAS visual analogue scale. View Large Table 3 Moderators of correlational pain assessment accuracy Moderator Categories Effect size (r) Number of effect sizes Fixed effects comparisons Year <1996 .45 30 0.00 ≥1996 .45 64 Country Asia/Middle East .51 8 17.17*** Europe .51 28 USA/Canada .43 54 Other .41 4 Patient Gender <50% male .46 33 1.60 ≥50% male .44 44 Patient Age Group Children .52 18 9.32* Adults .44 53 Older adults .45 7 Mixed .44 16 Observer type Informal caregivers .50 40 69.79*** Nurses .45 31 Physicians .45 15 Other healthcare providers .23 3 Other observers .15 5 Observer Gender <50% male .47 52 4.13* ≥50% male .54 5 Type of Pain Acute .45 39 2.72 Chronic .47 47 Mixed .35 4 Pain Condition Arthritis/musculoskeletal pain .28 12 110.69*** Burn .55 3 Cancer .51 35 Surgery or procedure .53 27 Laboratory .11 5 Assessment Method Faces Pain Scale .54 10 11.58** NRS/Likert .44 35 VAS .44 34 Other, mixed .46 14 Location of assessment Inpatient .49 32 77.22*** Outpatient .43 40 Laboratory .11 3 Mixed .54 19 Moderator Categories Effect size (r) Number of effect sizes Fixed effects comparisons Year <1996 .45 30 0.00 ≥1996 .45 64 Country Asia/Middle East .51 8 17.17*** Europe .51 28 USA/Canada .43 54 Other .41 4 Patient Gender <50% male .46 33 1.60 ≥50% male .44 44 Patient Age Group Children .52 18 9.32* Adults .44 53 Older adults .45 7 Mixed .44 16 Observer type Informal caregivers .50 40 69.79*** Nurses .45 31 Physicians .45 15 Other healthcare providers .23 3 Other observers .15 5 Observer Gender <50% male .47 52 4.13* ≥50% male .54 5 Type of Pain Acute .45 39 2.72 Chronic .47 47 Mixed .35 4 Pain Condition Arthritis/musculoskeletal pain .28 12 110.69*** Burn .55 3 Cancer .51 35 Surgery or procedure .53 27 Laboratory .11 5 Assessment Method Faces Pain Scale .54 10 11.58** NRS/Likert .44 35 VAS .44 34 Other, mixed .46 14 Location of assessment Inpatient .49 32 77.22*** Outpatient .43 40 Laboratory .11 3 Mixed .54 19 *p < .05; **p < .01; ***p < .001. NRS numeric rating scale; VAS visual analogue scale. View Large Characteristics of the patients: correlation The number of patients in the studies ranged from 8 to 1104 (M = 112.03, SD = 114.07). Patient age group (Q = 9.32, p < .05) significantly impacted pain assessment accuracy with accuracy highest when children were being assessed. Patient gender did not moderate correlational pain assessment accuracy (Q = 1.60, p = .21). Characteristics of the observer: correlation The number of observers in the study ranged from 5 to 1171 (M = 82.19, SD = 133.71). Observer characteristics including the observer type (Q = 69.62, p < .001) and gender (Q = 4.13, p < .05) were significant moderators in pain assessment accuracy. The 40 effect sizes which measured accuracy for caregivers such as a spouse or parent showed the highest accuracy (r = .50), while other healthcare providers (r = .23) and other observers (r = .15) showed the lowest accuracy levels. Fifty-seven studies reported on observer gender. Five studies that reported more than 50% of their observers were male had higher pain assessment accuracy (r = .54) than studies reporting a greater number of female observers (r = .47). Of the studies that documented an average number of years of clinical experience for healthcare providers (k = 14), a regression showed no impact as a significant moderator of pain assessment accuracy (β = −.02, 95% CI: −0.06, 0.01). Characteristics of the pain and assessment: correlation In order to be classified as acute, chronic, or mixed, studies had to state the type of pain experienced by the patients included in their sample. Pain assessment accuracy did not differ by pain type (acute, chronic, or a mix of the two). Observers were more accurate assessing pain related to burns, cancer, and surgeries or other procedures, than for arthritis, musculoskeletal, or laboratory-induced pain (Q = 110.69, p < .001). Pain assessments were less accurate in a laboratory or outpatient setting than in an inpatient or mixed setting (Q = 77.22, p < .001). Pain assessments were most accurate when using the Faces Pain Scale as opposed to the Numeric Rating Scale (NRS), Visual Analogue Scale (VAS), or other pain assessment methods (Q = 11.58, p < .01). Pain Assessment Accuracy: Paired Comparison Ninety articles yielded 103 independent effect sizes for the paired comparison meta-analysis (Table 4). The random effects mean effect size was d = −.26 (95% CI: −0.35, −0.17) and the fixed, weighted mean effect size was d = −.13 (95% CI: −0.15, −0.10) (Table 2). The significant combined Z = −10.88 (p < .001) indicates that observers significantly underestimated patient pain. It would take 4139 effect sizes (40.2 missing studies for each study included in the meta-analysis) to nullify the effects. The effect sizes were significantly heterogeneous so moderator analyses were conducted. Table 4 Characteristics and effect sizes of studies included in pain assessment paired comparison accuracy meta-analysis First author Study year Observer type Observer N Assessment method Effect size (d) SE 95% CI lower limit 95% CI upper limit Direction of pain assessment Akin [56] 2013 Caregivers 119 NRS/Likert .17 0.13 −0.08 0.42 Overestimation 2013 Nurses 119 NRS/Likert .00 0.13 −0.26 0.25 Bergh [57] 1999 Nurses 24 VAS .23 0.21 −0.18 0.63 Overestimation 1999 Nurses 8 VAS −.36 0.36 −1.07 0.36 Underestimation 1999 Nurses 7 VAS −1.65** 0.58 −2.79 −0.51 Underestimation Bowman [126] 1994 Nurses 16 VAS −1.06*** 0.31 −1.67 −0.44 Underestimation Broberger [59] 2005 Caregivers 52 NRS/Likert .32* 0.14 0.04 0.60 Overestimation 2005 Nurses 33 NRS/Likert .50** 0.18 0.14 0.87 Overestimation Cano [127] 2004 Caregivers 109 NRS/Likert .34*** 0.10 0.14 0.53 Overestimation Chambers [60] 1998 Caregivers 104 Faces −.15 0.10 −0.35 0.04 Underestimation Chambers [128] 1999 Caregivers 75 Faces .34** 0.12 0.11 0.57 Overestimation Coran [129] 2013 Physicians 10 NRS/Likert −.23 0.35 −0.91 0.45 Underestimation Dar [130] 1992 Caregivers 40 NRS/Likert .02 0.22 −0.42 0.46 Overestimation Dobkin [131] 2003 Physicians 182 VAS .19** 0.07 0.05 0.34 Overestimation Everett [69] 1994 Nurses 27 VAS .01 0.24 −0.46 0.48 Overestimation Forrest [70] 1989 Physicians 8 VAS -.53 0.38 −1.28 0.22 Underestimation Gil [72] 2004 Caregivers 35 NRS/Likert .33 0.24 −0.14 0.80 Overestimation Goulet [132] 2013 Other healthcare 1643 NRS/Likert −.08*** 0.02 −0.13 −0.03 Underestimation Green [133] 2009 Other observers 130 NRS/Likert −1.47*** 0.27 −1.99 −0.95 Underestimation Guru [134] 2000 Nurses 71 Other −.63*** 0.17 −0.96 −0.29 Underestimation 2000 Physicians 71 Other −.28 0.17 −0.61 0.06 Underestimation Hall-Lord [135] 1998 Nurses 44 NRS/Likert −.24 0.21 −0.65 0.16 Underestimation 1998 Nurses 37 NRS/Likert −.47* 0.22 −0.90 −0.04 Underestimation Hays [74] 1995 Caregivers 304 NRS/Likert .01 0.08 −0.15 0.17 Overestimation Heikkinen [75] 2005 Nurses 45 VAS −.09 0.21 −0.50 0.32 Underestimation Heuss [76] 2012 Nurses 12 VAS .44 0.30 −0.14 1.02 Overestimation 2012 Physicians 9 VAS .39 0.34 −0.28 1.06 Overestimation Higginson [77] 2008 Caregivers 64 VAS −.10 0.18 −0.44 0.25 Underestimation Hodgkins [78] 1985 Physicians 21 VAS −.23 0.31 −0.83 0.38 Underestimation 1985 Physicians 21 VAS −.33 0.22 −0.77 0.11 Underestimation Holmes [136] 1989 Nurses 53 VAS .32* 0.14 0.04 0.59 Overestimation Horgas [79] 2001 Nurses 16 Other .22 0.33 −0.43 0.86 Overestimation Idvall [137] 2002 Nurses 196 NRS/Likert −.27** 0.10 −0.47 −0.08 Underestimation Idvall [80] 2005 Nurses 267 NRS/Likert −.27** 0.09 −0.44 −0.10 Underestimation Kappesser [138] 2006 Other healthcare 120 Other −1.83*** 0.38 −2.58 −1.08 Underestimation Ketovuori [139] 1987 Nurses 62 Other .64* 0.25 0.14 1.13 Overestimation Kristjanson [82] 1998 Caregivers 78 Other .25 0.16 −0.06 0.57 Overestimation Krivo [140] 1996 Nurses 48 VAS −.75*** 0.21 −1.16 −0.34 Underestimation 1996 Physicians 50 VAS −.88*** 0.21 −1.29 −0.46 Underestimation Lamontagne [29] 1991 Nurses 13 VAS −.70* 0.31 −1.31 −0.10 Underestimation 1991 Physicians 13 VAS −1.03** 0.34 −1.71 −0.36 Underestimation Laugsand [141] 2010 Other healthcare 1928 NRS/Likert −.47*** 0.03 −0.53 −0.40 Underestimation Lieberman [142] 1996 Physicians 147 VAS −.27* 0.12 −0.50 −0.04 Underestimation Lin [83] 2001 Caregivers 89 NRS/Likert .07 0.11 −0.13 0.28 Overestimation Lobchuk [84] 1997 Caregivers 37 NRS/Likert .38 0.23 −0.08 0.84 Overestimation Lobchuk [20] 2002 Caregivers 98 Other .17 0.14 −0.11 0.45 Overestimation Madison [85] 1995 Caregivers 18 VAS .12 0.33 −0.53 0.78 Overestimation Maguire [86] 2014 Physicians 200 VAS −1.11*** 0.11 −1.32 −0.90 Underestimation Manne [87]a 1992 Nurses 9 Faces −.07 0.35 −0.75 0.62 Underestimation Mäntyselkä [5] 2001 Physicians 28 VAS −.16 0.19 −0.53 0.22 Underestimation Marquié [143] 2003 Physicians 172 VAS −.74*** 0.09 −0.91 −0.57 Underestimation Martire [144] 2006 Caregivers 137 VAS .49*** 0.12 0.25 0.73 Overestimation McMillan [89] 2003 Caregivers 264 NRS/Likert .42*** 0.06 0.29 0.54 Overestimation McPherson [90] 2008 Caregivers 66 NRS/Likert .19 0.12 −0.05 0.43 Overestimation Melotti [145] 2009 Nurses 17 NRS/Likert .11 0.34 −0.56 0.79 Overestimation Milne [146] 2006 Caregivers 51 Other .43* 0.20 0.04 0.82 Overestimation Modić Stanke [147] 2010 Nurses 31 NRS/Likert −.47 0.61 −1.66 0.72 Underestimation 2010 Other observers 32 NRS/Likert −.79 0.61 −1.99 0.41 Underestimation Molassiotis [148] 2010 Caregivers 82 NRS/Likert .00 0.15 −0.29 0.29 Oechsle [26] 2013 Caregivers 39 VAS .53* 0.23 0.08 0.98 Overestimation 2013 Physicians 40 NRS/Likert .00 0.22 −0.44 0.44 Ovayolu [149] 2015 Caregivers 220 NRS/Likert .08 0.10 −0.11 0.26 Overestimation Perreault [150] 2005 Other healthcare 78 NRS/Likert −.28* 0.12 −0.51 −0.06 Underestimation Perreault [95] 2006 Other healthcare 9 NRS/Likert −.26 0.35 −0.96 0.43 Underestimation Prkachin [97] 1994 Other observers 5 NRS/Likert −.39 0.49 −1.36 0.58 Underestimation Pronina [151] 2014 Other observers 120 Other −1.36*** 0.27 −1.88 −0.84 Underestimation Puntillo [152] 2003 Nurses 37 NRS/Likert −1.33*** 0.19 −1.70 −0.96 Underestimation Redinbaugh [99] 2002 Caregivers 31 VAS .88*** 0.21 0.47 1.29 Overestimation Riemsma [153] 2000 Caregivers 177 VAS .49*** 0.08 0.33 0.65 Overestimation Robinson [154] 2003 Other observers 29 VAS −1.81*** 0.42 −2.63 −0.98 Underestimation Ruben [7] 2013 Other observers 55 NRS/Likert −2.27*** 0.34 −2.94 −1.60 Underestimation 2013 Other observers 13 NRS/Likert −1.34*** 0.41 −2.15 −0.53 Underestimation 2013 Other observers 21 VAS −1.19*** 0.36 −1.89 −0.48 Underestimation Ruben [102] 2016 Other observers 172 VAS −.26*** 0.08 −0.41 −0.10 Underestimation Santos [155] 2014 Caregivers 75 Faces −4.66*** 0.31 −5.27 −4.04 Underestimation 2014 Caregivers 63 Faces −.43* 0.18 −0.79 −0.08 Underestimation Schneider [104] 1992 Caregivers 40 NRS/Likert −.16 0.16 −0.48 0.15 Underestimation 1992 Nurses 40 NRS/Likert −.43** 0.17 −0.75 −0.10 Underestimation Shugarman [156] 2010 Nurses 94 VAS −.29** 0.11 −0.51 −0.06 Underestimation Silveria [106] 2010 Caregivers 142 VAS .20* 0.08 0.03 0.37 Overestimation Sjöström [109] 1997 Other healthcare 60 Other −1.07*** 0.16 −1.37 −0.76 Underestimation Sloman [157] 2005 Nurses 95 Faces −.23* 0.10 −0.43 −0.03 Underestimation Sneeuw [110] 1997 Caregivers 103 Faces .11 0.10 −0.08 0.31 Overestimation Sneeuw [111] 1998 Caregivers 307 Faces .11* 0.06 0.00 0.22 Overestimation Sneeuw [112] 1999 Caregivers 90 VAS .25 0.15 −0.04 0.54 Overestimation 1999 Nurses 35 VAS −.09 0.20 −0.48 0.31 Underestimation 1999 Physicians 15 VAS −.34 0.28 −0.89 0.21 Underestimation Stalnikowicz [158] 2005 Nurses 70 VAS −.82*** 0.18 −1.16 −0.47 Underestimation 2005 Physicians 70 VAS −1.21*** 0.18 −1.57 −0.85 Underestimation Stephenson [113] 1994 Nurses 23 VAS −.31 0.21 −0.73 0.10 Underestimation Suarez-Almazor [115] 2001 Physicians 5 VAS −1.08* 0.46 −1.99 −0.17 Underestimation Sullivan [159] 2006 Other observers 60 NRS/Likert −1.96*** 0.37 −2.68 −1.24 Underestimation Sullivan [160] 2006 Other observers 20 NRS/Likert −.90** 0.29 −1.46 −0.34 Underestimation Sutherland [116] 1988 Physicians 22 VAS −.73*** 0.22 −1.16 −0.29 Underestimation Thomas [161] 1999 Physicians 30 VAS −.75** 0.27 −1.28 −0.23 Underestimation Todd [162] 1994 Physicians 65 VAS −.28* 0.14 −0.56 0.00 Underestimation Van der Does [118] 1989 Nurses 145 VAS .32** 0.12 0.08 0.56 Overestimation Vervoort [119] 2009 Caregivers 62 NRS/Likert −.01 0.13 −0.26 0.24 Underestimation Walkenstein [120] 1982 Nurses 44 Other −.49** 0.16 −0.80 −0.17 Underestimation Wennman-Larsen [122] 2007 Caregivers 54 NRS/Likert .20 0.14 −0.07 0.47 Overestimation Yeager [163] 1995 Caregivers 86 VAS .24* 0.11 0.02 0.45 Overestimation Yesilbalkan [123] 2010 Caregivers 80 NRS/Likert .10 0.11 −0.12 0.32 Overestimation Zalon [124] 1993 Nurses 119 VAS −.19* 0.09 −0.37 −0.01 Underestimation First author Study year Observer type Observer N Assessment method Effect size (d) SE 95% CI lower limit 95% CI upper limit Direction of pain assessment Akin [56] 2013 Caregivers 119 NRS/Likert .17 0.13 −0.08 0.42 Overestimation 2013 Nurses 119 NRS/Likert .00 0.13 −0.26 0.25 Bergh [57] 1999 Nurses 24 VAS .23 0.21 −0.18 0.63 Overestimation 1999 Nurses 8 VAS −.36 0.36 −1.07 0.36 Underestimation 1999 Nurses 7 VAS −1.65** 0.58 −2.79 −0.51 Underestimation Bowman [126] 1994 Nurses 16 VAS −1.06*** 0.31 −1.67 −0.44 Underestimation Broberger [59] 2005 Caregivers 52 NRS/Likert .32* 0.14 0.04 0.60 Overestimation 2005 Nurses 33 NRS/Likert .50** 0.18 0.14 0.87 Overestimation Cano [127] 2004 Caregivers 109 NRS/Likert .34*** 0.10 0.14 0.53 Overestimation Chambers [60] 1998 Caregivers 104 Faces −.15 0.10 −0.35 0.04 Underestimation Chambers [128] 1999 Caregivers 75 Faces .34** 0.12 0.11 0.57 Overestimation Coran [129] 2013 Physicians 10 NRS/Likert −.23 0.35 −0.91 0.45 Underestimation Dar [130] 1992 Caregivers 40 NRS/Likert .02 0.22 −0.42 0.46 Overestimation Dobkin [131] 2003 Physicians 182 VAS .19** 0.07 0.05 0.34 Overestimation Everett [69] 1994 Nurses 27 VAS .01 0.24 −0.46 0.48 Overestimation Forrest [70] 1989 Physicians 8 VAS -.53 0.38 −1.28 0.22 Underestimation Gil [72] 2004 Caregivers 35 NRS/Likert .33 0.24 −0.14 0.80 Overestimation Goulet [132] 2013 Other healthcare 1643 NRS/Likert −.08*** 0.02 −0.13 −0.03 Underestimation Green [133] 2009 Other observers 130 NRS/Likert −1.47*** 0.27 −1.99 −0.95 Underestimation Guru [134] 2000 Nurses 71 Other −.63*** 0.17 −0.96 −0.29 Underestimation 2000 Physicians 71 Other −.28 0.17 −0.61 0.06 Underestimation Hall-Lord [135] 1998 Nurses 44 NRS/Likert −.24 0.21 −0.65 0.16 Underestimation 1998 Nurses 37 NRS/Likert −.47* 0.22 −0.90 −0.04 Underestimation Hays [74] 1995 Caregivers 304 NRS/Likert .01 0.08 −0.15 0.17 Overestimation Heikkinen [75] 2005 Nurses 45 VAS −.09 0.21 −0.50 0.32 Underestimation Heuss [76] 2012 Nurses 12 VAS .44 0.30 −0.14 1.02 Overestimation 2012 Physicians 9 VAS .39 0.34 −0.28 1.06 Overestimation Higginson [77] 2008 Caregivers 64 VAS −.10 0.18 −0.44 0.25 Underestimation Hodgkins [78] 1985 Physicians 21 VAS −.23 0.31 −0.83 0.38 Underestimation 1985 Physicians 21 VAS −.33 0.22 −0.77 0.11 Underestimation Holmes [136] 1989 Nurses 53 VAS .32* 0.14 0.04 0.59 Overestimation Horgas [79] 2001 Nurses 16 Other .22 0.33 −0.43 0.86 Overestimation Idvall [137] 2002 Nurses 196 NRS/Likert −.27** 0.10 −0.47 −0.08 Underestimation Idvall [80] 2005 Nurses 267 NRS/Likert −.27** 0.09 −0.44 −0.10 Underestimation Kappesser [138] 2006 Other healthcare 120 Other −1.83*** 0.38 −2.58 −1.08 Underestimation Ketovuori [139] 1987 Nurses 62 Other .64* 0.25 0.14 1.13 Overestimation Kristjanson [82] 1998 Caregivers 78 Other .25 0.16 −0.06 0.57 Overestimation Krivo [140] 1996 Nurses 48 VAS −.75*** 0.21 −1.16 −0.34 Underestimation 1996 Physicians 50 VAS −.88*** 0.21 −1.29 −0.46 Underestimation Lamontagne [29] 1991 Nurses 13 VAS −.70* 0.31 −1.31 −0.10 Underestimation 1991 Physicians 13 VAS −1.03** 0.34 −1.71 −0.36 Underestimation Laugsand [141] 2010 Other healthcare 1928 NRS/Likert −.47*** 0.03 −0.53 −0.40 Underestimation Lieberman [142] 1996 Physicians 147 VAS −.27* 0.12 −0.50 −0.04 Underestimation Lin [83] 2001 Caregivers 89 NRS/Likert .07 0.11 −0.13 0.28 Overestimation Lobchuk [84] 1997 Caregivers 37 NRS/Likert .38 0.23 −0.08 0.84 Overestimation Lobchuk [20] 2002 Caregivers 98 Other .17 0.14 −0.11 0.45 Overestimation Madison [85] 1995 Caregivers 18 VAS .12 0.33 −0.53 0.78 Overestimation Maguire [86] 2014 Physicians 200 VAS −1.11*** 0.11 −1.32 −0.90 Underestimation Manne [87]a 1992 Nurses 9 Faces −.07 0.35 −0.75 0.62 Underestimation Mäntyselkä [5] 2001 Physicians 28 VAS −.16 0.19 −0.53 0.22 Underestimation Marquié [143] 2003 Physicians 172 VAS −.74*** 0.09 −0.91 −0.57 Underestimation Martire [144] 2006 Caregivers 137 VAS .49*** 0.12 0.25 0.73 Overestimation McMillan [89] 2003 Caregivers 264 NRS/Likert .42*** 0.06 0.29 0.54 Overestimation McPherson [90] 2008 Caregivers 66 NRS/Likert .19 0.12 −0.05 0.43 Overestimation Melotti [145] 2009 Nurses 17 NRS/Likert .11 0.34 −0.56 0.79 Overestimation Milne [146] 2006 Caregivers 51 Other .43* 0.20 0.04 0.82 Overestimation Modić Stanke [147] 2010 Nurses 31 NRS/Likert −.47 0.61 −1.66 0.72 Underestimation 2010 Other observers 32 NRS/Likert −.79 0.61 −1.99 0.41 Underestimation Molassiotis [148] 2010 Caregivers 82 NRS/Likert .00 0.15 −0.29 0.29 Oechsle [26] 2013 Caregivers 39 VAS .53* 0.23 0.08 0.98 Overestimation 2013 Physicians 40 NRS/Likert .00 0.22 −0.44 0.44 Ovayolu [149] 2015 Caregivers 220 NRS/Likert .08 0.10 −0.11 0.26 Overestimation Perreault [150] 2005 Other healthcare 78 NRS/Likert −.28* 0.12 −0.51 −0.06 Underestimation Perreault [95] 2006 Other healthcare 9 NRS/Likert −.26 0.35 −0.96 0.43 Underestimation Prkachin [97] 1994 Other observers 5 NRS/Likert −.39 0.49 −1.36 0.58 Underestimation Pronina [151] 2014 Other observers 120 Other −1.36*** 0.27 −1.88 −0.84 Underestimation Puntillo [152] 2003 Nurses 37 NRS/Likert −1.33*** 0.19 −1.70 −0.96 Underestimation Redinbaugh [99] 2002 Caregivers 31 VAS .88*** 0.21 0.47 1.29 Overestimation Riemsma [153] 2000 Caregivers 177 VAS .49*** 0.08 0.33 0.65 Overestimation Robinson [154] 2003 Other observers 29 VAS −1.81*** 0.42 −2.63 −0.98 Underestimation Ruben [7] 2013 Other observers 55 NRS/Likert −2.27*** 0.34 −2.94 −1.60 Underestimation 2013 Other observers 13 NRS/Likert −1.34*** 0.41 −2.15 −0.53 Underestimation 2013 Other observers 21 VAS −1.19*** 0.36 −1.89 −0.48 Underestimation Ruben [102] 2016 Other observers 172 VAS −.26*** 0.08 −0.41 −0.10 Underestimation Santos [155] 2014 Caregivers 75 Faces −4.66*** 0.31 −5.27 −4.04 Underestimation 2014 Caregivers 63 Faces −.43* 0.18 −0.79 −0.08 Underestimation Schneider [104] 1992 Caregivers 40 NRS/Likert −.16 0.16 −0.48 0.15 Underestimation 1992 Nurses 40 NRS/Likert −.43** 0.17 −0.75 −0.10 Underestimation Shugarman [156] 2010 Nurses 94 VAS −.29** 0.11 −0.51 −0.06 Underestimation Silveria [106] 2010 Caregivers 142 VAS .20* 0.08 0.03 0.37 Overestimation Sjöström [109] 1997 Other healthcare 60 Other −1.07*** 0.16 −1.37 −0.76 Underestimation Sloman [157] 2005 Nurses 95 Faces −.23* 0.10 −0.43 −0.03 Underestimation Sneeuw [110] 1997 Caregivers 103 Faces .11 0.10 −0.08 0.31 Overestimation Sneeuw [111] 1998 Caregivers 307 Faces .11* 0.06 0.00 0.22 Overestimation Sneeuw [112] 1999 Caregivers 90 VAS .25 0.15 −0.04 0.54 Overestimation 1999 Nurses 35 VAS −.09 0.20 −0.48 0.31 Underestimation 1999 Physicians 15 VAS −.34 0.28 −0.89 0.21 Underestimation Stalnikowicz [158] 2005 Nurses 70 VAS −.82*** 0.18 −1.16 −0.47 Underestimation 2005 Physicians 70 VAS −1.21*** 0.18 −1.57 −0.85 Underestimation Stephenson [113] 1994 Nurses 23 VAS −.31 0.21 −0.73 0.10 Underestimation Suarez-Almazor [115] 2001 Physicians 5 VAS −1.08* 0.46 −1.99 −0.17 Underestimation Sullivan [159] 2006 Other observers 60 NRS/Likert −1.96*** 0.37 −2.68 −1.24 Underestimation Sullivan [160] 2006 Other observers 20 NRS/Likert −.90** 0.29 −1.46 −0.34 Underestimation Sutherland [116] 1988 Physicians 22 VAS −.73*** 0.22 −1.16 −0.29 Underestimation Thomas [161] 1999 Physicians 30 VAS −.75** 0.27 −1.28 −0.23 Underestimation Todd [162] 1994 Physicians 65 VAS −.28* 0.14 −0.56 0.00 Underestimation Van der Does [118] 1989 Nurses 145 VAS .32** 0.12 0.08 0.56 Overestimation Vervoort [119] 2009 Caregivers 62 NRS/Likert −.01 0.13 −0.26 0.24 Underestimation Walkenstein [120] 1982 Nurses 44 Other −.49** 0.16 −0.80 −0.17 Underestimation Wennman-Larsen [122] 2007 Caregivers 54 NRS/Likert .20 0.14 −0.07 0.47 Overestimation Yeager [163] 1995 Caregivers 86 VAS .24* 0.11 0.02 0.45 Overestimation Yesilbalkan [123] 2010 Caregivers 80 NRS/Likert .10 0.11 −0.12 0.32 Overestimation Zalon [124] 1993 Nurses 119 VAS −.19* 0.09 −0.37 −0.01 Underestimation aStudies used different pain scales for the patients and observers and we standardized results for analysis. d is standard difference in means. SE is standard error. NRS is numeric rating scale, VAS is visual analogue scale. Faces is Faces Pain Scale. *p < .05; **p < .01; ***p < .001. View Large Table 4 Characteristics and effect sizes of studies included in pain assessment paired comparison accuracy meta-analysis First author Study year Observer type Observer N Assessment method Effect size (d) SE 95% CI lower limit 95% CI upper limit Direction of pain assessment Akin [56] 2013 Caregivers 119 NRS/Likert .17 0.13 −0.08 0.42 Overestimation 2013 Nurses 119 NRS/Likert .00 0.13 −0.26 0.25 Bergh [57] 1999 Nurses 24 VAS .23 0.21 −0.18 0.63 Overestimation 1999 Nurses 8 VAS −.36 0.36 −1.07 0.36 Underestimation 1999 Nurses 7 VAS −1.65** 0.58 −2.79 −0.51 Underestimation Bowman [126] 1994 Nurses 16 VAS −1.06*** 0.31 −1.67 −0.44 Underestimation Broberger [59] 2005 Caregivers 52 NRS/Likert .32* 0.14 0.04 0.60 Overestimation 2005 Nurses 33 NRS/Likert .50** 0.18 0.14 0.87 Overestimation Cano [127] 2004 Caregivers 109 NRS/Likert .34*** 0.10 0.14 0.53 Overestimation Chambers [60] 1998 Caregivers 104 Faces −.15 0.10 −0.35 0.04 Underestimation Chambers [128] 1999 Caregivers 75 Faces .34** 0.12 0.11 0.57 Overestimation Coran [129] 2013 Physicians 10 NRS/Likert −.23 0.35 −0.91 0.45 Underestimation Dar [130] 1992 Caregivers 40 NRS/Likert .02 0.22 −0.42 0.46 Overestimation Dobkin [131] 2003 Physicians 182 VAS .19** 0.07 0.05 0.34 Overestimation Everett [69] 1994 Nurses 27 VAS .01 0.24 −0.46 0.48 Overestimation Forrest [70] 1989 Physicians 8 VAS -.53 0.38 −1.28 0.22 Underestimation Gil [72] 2004 Caregivers 35 NRS/Likert .33 0.24 −0.14 0.80 Overestimation Goulet [132] 2013 Other healthcare 1643 NRS/Likert −.08*** 0.02 −0.13 −0.03 Underestimation Green [133] 2009 Other observers 130 NRS/Likert −1.47*** 0.27 −1.99 −0.95 Underestimation Guru [134] 2000 Nurses 71 Other −.63*** 0.17 −0.96 −0.29 Underestimation 2000 Physicians 71 Other −.28 0.17 −0.61 0.06 Underestimation Hall-Lord [135] 1998 Nurses 44 NRS/Likert −.24 0.21 −0.65 0.16 Underestimation 1998 Nurses 37 NRS/Likert −.47* 0.22 −0.90 −0.04 Underestimation Hays [74] 1995 Caregivers 304 NRS/Likert .01 0.08 −0.15 0.17 Overestimation Heikkinen [75] 2005 Nurses 45 VAS −.09 0.21 −0.50 0.32 Underestimation Heuss [76] 2012 Nurses 12 VAS .44 0.30 −0.14 1.02 Overestimation 2012 Physicians 9 VAS .39 0.34 −0.28 1.06 Overestimation Higginson [77] 2008 Caregivers 64 VAS −.10 0.18 −0.44 0.25 Underestimation Hodgkins [78] 1985 Physicians 21 VAS −.23 0.31 −0.83 0.38 Underestimation 1985 Physicians 21 VAS −.33 0.22 −0.77 0.11 Underestimation Holmes [136] 1989 Nurses 53 VAS .32* 0.14 0.04 0.59 Overestimation Horgas [79] 2001 Nurses 16 Other .22 0.33 −0.43 0.86 Overestimation Idvall [137] 2002 Nurses 196 NRS/Likert −.27** 0.10 −0.47 −0.08 Underestimation Idvall [80] 2005 Nurses 267 NRS/Likert −.27** 0.09 −0.44 −0.10 Underestimation Kappesser [138] 2006 Other healthcare 120 Other −1.83*** 0.38 −2.58 −1.08 Underestimation Ketovuori [139] 1987 Nurses 62 Other .64* 0.25 0.14 1.13 Overestimation Kristjanson [82] 1998 Caregivers 78 Other .25 0.16 −0.06 0.57 Overestimation Krivo [140] 1996 Nurses 48 VAS −.75*** 0.21 −1.16 −0.34 Underestimation 1996 Physicians 50 VAS −.88*** 0.21 −1.29 −0.46 Underestimation Lamontagne [29] 1991 Nurses 13 VAS −.70* 0.31 −1.31 −0.10 Underestimation 1991 Physicians 13 VAS −1.03** 0.34 −1.71 −0.36 Underestimation Laugsand [141] 2010 Other healthcare 1928 NRS/Likert −.47*** 0.03 −0.53 −0.40 Underestimation Lieberman [142] 1996 Physicians 147 VAS −.27* 0.12 −0.50 −0.04 Underestimation Lin [83] 2001 Caregivers 89 NRS/Likert .07 0.11 −0.13 0.28 Overestimation Lobchuk [84] 1997 Caregivers 37 NRS/Likert .38 0.23 −0.08 0.84 Overestimation Lobchuk [20] 2002 Caregivers 98 Other .17 0.14 −0.11 0.45 Overestimation Madison [85] 1995 Caregivers 18 VAS .12 0.33 −0.53 0.78 Overestimation Maguire [86] 2014 Physicians 200 VAS −1.11*** 0.11 −1.32 −0.90 Underestimation Manne [87]a 1992 Nurses 9 Faces −.07 0.35 −0.75 0.62 Underestimation Mäntyselkä [5] 2001 Physicians 28 VAS −.16 0.19 −0.53 0.22 Underestimation Marquié [143] 2003 Physicians 172 VAS −.74*** 0.09 −0.91 −0.57 Underestimation Martire [144] 2006 Caregivers 137 VAS .49*** 0.12 0.25 0.73 Overestimation McMillan [89] 2003 Caregivers 264 NRS/Likert .42*** 0.06 0.29 0.54 Overestimation McPherson [90] 2008 Caregivers 66 NRS/Likert .19 0.12 −0.05 0.43 Overestimation Melotti [145] 2009 Nurses 17 NRS/Likert .11 0.34 −0.56 0.79 Overestimation Milne [146] 2006 Caregivers 51 Other .43* 0.20 0.04 0.82 Overestimation Modić Stanke [147] 2010 Nurses 31 NRS/Likert −.47 0.61 −1.66 0.72 Underestimation 2010 Other observers 32 NRS/Likert −.79 0.61 −1.99 0.41 Underestimation Molassiotis [148] 2010 Caregivers 82 NRS/Likert .00 0.15 −0.29 0.29 Oechsle [26] 2013 Caregivers 39 VAS .53* 0.23 0.08 0.98 Overestimation 2013 Physicians 40 NRS/Likert .00 0.22 −0.44 0.44 Ovayolu [149] 2015 Caregivers 220 NRS/Likert .08 0.10 −0.11 0.26 Overestimation Perreault [150] 2005 Other healthcare 78 NRS/Likert −.28* 0.12 −0.51 −0.06 Underestimation Perreault [95] 2006 Other healthcare 9 NRS/Likert −.26 0.35 −0.96 0.43 Underestimation Prkachin [97] 1994 Other observers 5 NRS/Likert −.39 0.49 −1.36 0.58 Underestimation Pronina [151] 2014 Other observers 120 Other −1.36*** 0.27 −1.88 −0.84 Underestimation Puntillo [152] 2003 Nurses 37 NRS/Likert −1.33*** 0.19 −1.70 −0.96 Underestimation Redinbaugh [99] 2002 Caregivers 31 VAS .88*** 0.21 0.47 1.29 Overestimation Riemsma [153] 2000 Caregivers 177 VAS .49*** 0.08 0.33 0.65 Overestimation Robinson [154] 2003 Other observers 29 VAS −1.81*** 0.42 −2.63 −0.98 Underestimation Ruben [7] 2013 Other observers 55 NRS/Likert −2.27*** 0.34 −2.94 −1.60 Underestimation 2013 Other observers 13 NRS/Likert −1.34*** 0.41 −2.15 −0.53 Underestimation 2013 Other observers 21 VAS −1.19*** 0.36 −1.89 −0.48 Underestimation Ruben [102] 2016 Other observers 172 VAS −.26*** 0.08 −0.41 −0.10 Underestimation Santos [155] 2014 Caregivers 75 Faces −4.66*** 0.31 −5.27 −4.04 Underestimation 2014 Caregivers 63 Faces −.43* 0.18 −0.79 −0.08 Underestimation Schneider [104] 1992 Caregivers 40 NRS/Likert −.16 0.16 −0.48 0.15 Underestimation 1992 Nurses 40 NRS/Likert −.43** 0.17 −0.75 −0.10 Underestimation Shugarman [156] 2010 Nurses 94 VAS −.29** 0.11 −0.51 −0.06 Underestimation Silveria [106] 2010 Caregivers 142 VAS .20* 0.08 0.03 0.37 Overestimation Sjöström [109] 1997 Other healthcare 60 Other −1.07*** 0.16 −1.37 −0.76 Underestimation Sloman [157] 2005 Nurses 95 Faces −.23* 0.10 −0.43 −0.03 Underestimation Sneeuw [110] 1997 Caregivers 103 Faces .11 0.10 −0.08 0.31 Overestimation Sneeuw [111] 1998 Caregivers 307 Faces .11* 0.06 0.00 0.22 Overestimation Sneeuw [112] 1999 Caregivers 90 VAS .25 0.15 −0.04 0.54 Overestimation 1999 Nurses 35 VAS −.09 0.20 −0.48 0.31 Underestimation 1999 Physicians 15 VAS −.34 0.28 −0.89 0.21 Underestimation Stalnikowicz [158] 2005 Nurses 70 VAS −.82*** 0.18 −1.16 −0.47 Underestimation 2005 Physicians 70 VAS −1.21*** 0.18 −1.57 −0.85 Underestimation Stephenson [113] 1994 Nurses 23 VAS −.31 0.21 −0.73 0.10 Underestimation Suarez-Almazor [115] 2001 Physicians 5 VAS −1.08* 0.46 −1.99 −0.17 Underestimation Sullivan [159] 2006 Other observers 60 NRS/Likert −1.96*** 0.37 −2.68 −1.24 Underestimation Sullivan [160] 2006 Other observers 20 NRS/Likert −.90** 0.29 −1.46 −0.34 Underestimation Sutherland [116] 1988 Physicians 22 VAS −.73*** 0.22 −1.16 −0.29 Underestimation Thomas [161] 1999 Physicians 30 VAS −.75** 0.27 −1.28 −0.23 Underestimation Todd [162] 1994 Physicians 65 VAS −.28* 0.14 −0.56 0.00 Underestimation Van der Does [118] 1989 Nurses 145 VAS .32** 0.12 0.08 0.56 Overestimation Vervoort [119] 2009 Caregivers 62 NRS/Likert −.01 0.13 −0.26 0.24 Underestimation Walkenstein [120] 1982 Nurses 44 Other −.49** 0.16 −0.80 −0.17 Underestimation Wennman-Larsen [122] 2007 Caregivers 54 NRS/Likert .20 0.14 −0.07 0.47 Overestimation Yeager [163] 1995 Caregivers 86 VAS .24* 0.11 0.02 0.45 Overestimation Yesilbalkan [123] 2010 Caregivers 80 NRS/Likert .10 0.11 −0.12 0.32 Overestimation Zalon [124] 1993 Nurses 119 VAS −.19* 0.09 −0.37 −0.01 Underestimation First author Study year Observer type Observer N Assessment method Effect size (d) SE 95% CI lower limit 95% CI upper limit Direction of pain assessment Akin [56] 2013 Caregivers 119 NRS/Likert .17 0.13 −0.08 0.42 Overestimation 2013 Nurses 119 NRS/Likert .00 0.13 −0.26 0.25 Bergh [57] 1999 Nurses 24 VAS .23 0.21 −0.18 0.63 Overestimation 1999 Nurses 8 VAS −.36 0.36 −1.07 0.36 Underestimation 1999 Nurses 7 VAS −1.65** 0.58 −2.79 −0.51 Underestimation Bowman [126] 1994 Nurses 16 VAS −1.06*** 0.31 −1.67 −0.44 Underestimation Broberger [59] 2005 Caregivers 52 NRS/Likert .32* 0.14 0.04 0.60 Overestimation 2005 Nurses 33 NRS/Likert .50** 0.18 0.14 0.87 Overestimation Cano [127] 2004 Caregivers 109 NRS/Likert .34*** 0.10 0.14 0.53 Overestimation Chambers [60] 1998 Caregivers 104 Faces −.15 0.10 −0.35 0.04 Underestimation Chambers [128] 1999 Caregivers 75 Faces .34** 0.12 0.11 0.57 Overestimation Coran [129] 2013 Physicians 10 NRS/Likert −.23 0.35 −0.91 0.45 Underestimation Dar [130] 1992 Caregivers 40 NRS/Likert .02 0.22 −0.42 0.46 Overestimation Dobkin [131] 2003 Physicians 182 VAS .19** 0.07 0.05 0.34 Overestimation Everett [69] 1994 Nurses 27 VAS .01 0.24 −0.46 0.48 Overestimation Forrest [70] 1989 Physicians 8 VAS -.53 0.38 −1.28 0.22 Underestimation Gil [72] 2004 Caregivers 35 NRS/Likert .33 0.24 −0.14 0.80 Overestimation Goulet [132] 2013 Other healthcare 1643 NRS/Likert −.08*** 0.02 −0.13 −0.03 Underestimation Green [133] 2009 Other observers 130 NRS/Likert −1.47*** 0.27 −1.99 −0.95 Underestimation Guru [134] 2000 Nurses 71 Other −.63*** 0.17 −0.96 −0.29 Underestimation 2000 Physicians 71 Other −.28 0.17 −0.61 0.06 Underestimation Hall-Lord [135] 1998 Nurses 44 NRS/Likert −.24 0.21 −0.65 0.16 Underestimation 1998 Nurses 37 NRS/Likert −.47* 0.22 −0.90 −0.04 Underestimation Hays [74] 1995 Caregivers 304 NRS/Likert .01 0.08 −0.15 0.17 Overestimation Heikkinen [75] 2005 Nurses 45 VAS −.09 0.21 −0.50 0.32 Underestimation Heuss [76] 2012 Nurses 12 VAS .44 0.30 −0.14 1.02 Overestimation 2012 Physicians 9 VAS .39 0.34 −0.28 1.06 Overestimation Higginson [77] 2008 Caregivers 64 VAS −.10 0.18 −0.44 0.25 Underestimation Hodgkins [78] 1985 Physicians 21 VAS −.23 0.31 −0.83 0.38 Underestimation 1985 Physicians 21 VAS −.33 0.22 −0.77 0.11 Underestimation Holmes [136] 1989 Nurses 53 VAS .32* 0.14 0.04 0.59 Overestimation Horgas [79] 2001 Nurses 16 Other .22 0.33 −0.43 0.86 Overestimation Idvall [137] 2002 Nurses 196 NRS/Likert −.27** 0.10 −0.47 −0.08 Underestimation Idvall [80] 2005 Nurses 267 NRS/Likert −.27** 0.09 −0.44 −0.10 Underestimation Kappesser [138] 2006 Other healthcare 120 Other −1.83*** 0.38 −2.58 −1.08 Underestimation Ketovuori [139] 1987 Nurses 62 Other .64* 0.25 0.14 1.13 Overestimation Kristjanson [82] 1998 Caregivers 78 Other .25 0.16 −0.06 0.57 Overestimation Krivo [140] 1996 Nurses 48 VAS −.75*** 0.21 −1.16 −0.34 Underestimation 1996 Physicians 50 VAS −.88*** 0.21 −1.29 −0.46 Underestimation Lamontagne [29] 1991 Nurses 13 VAS −.70* 0.31 −1.31 −0.10 Underestimation 1991 Physicians 13 VAS −1.03** 0.34 −1.71 −0.36 Underestimation Laugsand [141] 2010 Other healthcare 1928 NRS/Likert −.47*** 0.03 −0.53 −0.40 Underestimation Lieberman [142] 1996 Physicians 147 VAS −.27* 0.12 −0.50 −0.04 Underestimation Lin [83] 2001 Caregivers 89 NRS/Likert .07 0.11 −0.13 0.28 Overestimation Lobchuk [84] 1997 Caregivers 37 NRS/Likert .38 0.23 −0.08 0.84 Overestimation Lobchuk [20] 2002 Caregivers 98 Other .17 0.14 −0.11 0.45 Overestimation Madison [85] 1995 Caregivers 18 VAS .12 0.33 −0.53 0.78 Overestimation Maguire [86] 2014 Physicians 200 VAS −1.11*** 0.11 −1.32 −0.90 Underestimation Manne [87]a 1992 Nurses 9 Faces −.07 0.35 −0.75 0.62 Underestimation Mäntyselkä [5] 2001 Physicians 28 VAS −.16 0.19 −0.53 0.22 Underestimation Marquié [143] 2003 Physicians 172 VAS −.74*** 0.09 −0.91 −0.57 Underestimation Martire [144] 2006 Caregivers 137 VAS .49*** 0.12 0.25 0.73 Overestimation McMillan [89] 2003 Caregivers 264 NRS/Likert .42*** 0.06 0.29 0.54 Overestimation McPherson [90] 2008 Caregivers 66 NRS/Likert .19 0.12 −0.05 0.43 Overestimation Melotti [145] 2009 Nurses 17 NRS/Likert .11 0.34 −0.56 0.79 Overestimation Milne [146] 2006 Caregivers 51 Other .43* 0.20 0.04 0.82 Overestimation Modić Stanke [147] 2010 Nurses 31 NRS/Likert −.47 0.61 −1.66 0.72 Underestimation 2010 Other observers 32 NRS/Likert −.79 0.61 −1.99 0.41 Underestimation Molassiotis [148] 2010 Caregivers 82 NRS/Likert .00 0.15 −0.29 0.29 Oechsle [26] 2013 Caregivers 39 VAS .53* 0.23 0.08 0.98 Overestimation 2013 Physicians 40 NRS/Likert .00 0.22 −0.44 0.44 Ovayolu [149] 2015 Caregivers 220 NRS/Likert .08 0.10 −0.11 0.26 Overestimation Perreault [150] 2005 Other healthcare 78 NRS/Likert −.28* 0.12 −0.51 −0.06 Underestimation Perreault [95] 2006 Other healthcare 9 NRS/Likert −.26 0.35 −0.96 0.43 Underestimation Prkachin [97] 1994 Other observers 5 NRS/Likert −.39 0.49 −1.36 0.58 Underestimation Pronina [151] 2014 Other observers 120 Other −1.36*** 0.27 −1.88 −0.84 Underestimation Puntillo [152] 2003 Nurses 37 NRS/Likert −1.33*** 0.19 −1.70 −0.96 Underestimation Redinbaugh [99] 2002 Caregivers 31 VAS .88*** 0.21 0.47 1.29 Overestimation Riemsma [153] 2000 Caregivers 177 VAS .49*** 0.08 0.33 0.65 Overestimation Robinson [154] 2003 Other observers 29 VAS −1.81*** 0.42 −2.63 −0.98 Underestimation Ruben [7] 2013 Other observers 55 NRS/Likert −2.27*** 0.34 −2.94 −1.60 Underestimation 2013 Other observers 13 NRS/Likert −1.34*** 0.41 −2.15 −0.53 Underestimation 2013 Other observers 21 VAS −1.19*** 0.36 −1.89 −0.48 Underestimation Ruben [102] 2016 Other observers 172 VAS −.26*** 0.08 −0.41 −0.10 Underestimation Santos [155] 2014 Caregivers 75 Faces −4.66*** 0.31 −5.27 −4.04 Underestimation 2014 Caregivers 63 Faces −.43* 0.18 −0.79 −0.08 Underestimation Schneider [104] 1992 Caregivers 40 NRS/Likert −.16 0.16 −0.48 0.15 Underestimation 1992 Nurses 40 NRS/Likert −.43** 0.17 −0.75 −0.10 Underestimation Shugarman [156] 2010 Nurses 94 VAS −.29** 0.11 −0.51 −0.06 Underestimation Silveria [106] 2010 Caregivers 142 VAS .20* 0.08 0.03 0.37 Overestimation Sjöström [109] 1997 Other healthcare 60 Other −1.07*** 0.16 −1.37 −0.76 Underestimation Sloman [157] 2005 Nurses 95 Faces −.23* 0.10 −0.43 −0.03 Underestimation Sneeuw [110] 1997 Caregivers 103 Faces .11 0.10 −0.08 0.31 Overestimation Sneeuw [111] 1998 Caregivers 307 Faces .11* 0.06 0.00 0.22 Overestimation Sneeuw [112] 1999 Caregivers 90 VAS .25 0.15 −0.04 0.54 Overestimation 1999 Nurses 35 VAS −.09 0.20 −0.48 0.31 Underestimation 1999 Physicians 15 VAS −.34 0.28 −0.89 0.21 Underestimation Stalnikowicz [158] 2005 Nurses 70 VAS −.82*** 0.18 −1.16 −0.47 Underestimation 2005 Physicians 70 VAS −1.21*** 0.18 −1.57 −0.85 Underestimation Stephenson [113] 1994 Nurses 23 VAS −.31 0.21 −0.73 0.10 Underestimation Suarez-Almazor [115] 2001 Physicians 5 VAS −1.08* 0.46 −1.99 −0.17 Underestimation Sullivan [159] 2006 Other observers 60 NRS/Likert −1.96*** 0.37 −2.68 −1.24 Underestimation Sullivan [160] 2006 Other observers 20 NRS/Likert −.90** 0.29 −1.46 −0.34 Underestimation Sutherland [116] 1988 Physicians 22 VAS −.73*** 0.22 −1.16 −0.29 Underestimation Thomas [161] 1999 Physicians 30 VAS −.75** 0.27 −1.28 −0.23 Underestimation Todd [162] 1994 Physicians 65 VAS −.28* 0.14 −0.56 0.00 Underestimation Van der Does [118] 1989 Nurses 145 VAS .32** 0.12 0.08 0.56 Overestimation Vervoort [119] 2009 Caregivers 62 NRS/Likert −.01 0.13 −0.26 0.24 Underestimation Walkenstein [120] 1982 Nurses 44 Other −.49** 0.16 −0.80 −0.17 Underestimation Wennman-Larsen [122] 2007 Caregivers 54 NRS/Likert .20 0.14 −0.07 0.47 Overestimation Yeager [163] 1995 Caregivers 86 VAS .24* 0.11 0.02 0.45 Overestimation Yesilbalkan [123] 2010 Caregivers 80 NRS/Likert .10 0.11 −0.12 0.32 Overestimation Zalon [124] 1993 Nurses 119 VAS −.19* 0.09 −0.37 −0.01 Underestimation aStudies used different pain scales for the patients and observers and we standardized results for analysis. d is standard difference in means. SE is standard error. NRS is numeric rating scale, VAS is visual analogue scale. Faces is Faces Pain Scale. *p < .05; **p < .01; ***p < .001. View Large Characteristics of the study: paired comparison Studies were published from 1982 to 2015. Study year was not significant as a continuous moderator (β = −.01. 95% CI: −0.02, 0.00) or in a dichotomous analysis examining differences in the effect size before or after 1996 (Table 5). The majority of studies were conducted in the USA or Canada (52%) or in Europe (36%). Studies conducted in Europe and the USA/Canada showed significantly more underestimation than studies conducted elsewhere (Q =38.09, p < .001). Table 5 Moderators of paired comparison pain assessment accuracy Moderator Categories Effect size da (SE) Number of effect sizes Fixed effects comparisons For pain assessment accuracy reported as paired comparisons Year <1996 −.18 (0.03) 23 2.28 ≥1996 −.12 (0.01) 80 Country Asia/Middle East −.08 (0.04) 9 38.09*** Europe −.21 (0.02) 37 USA/Canada −.10 (0.02) 54 Other .19 (0.08) 3 Patient Gender <50% male −.02 (0.02) 46 35.87*** ≥50% male −.17 (0.02) 41 Patient Age Group Children −.09 (0.05) 8 89.68*** Adults −.08 (0.01) 62 Older adults −.22 (0.07) 9 Mixed −.44 (0.04) 24 Observer type Informal caregivers .17 (0.02) 34 396.87*** Nurses −.21 (0.03) 32 Physicians −.41 (0.04) 20 Other healthcare providers −.24 (0.02) 6 Other observers −.64 (0.06) 11 Observer Gender <50% male .02 (0.02) 52 .09 ≥50 % male .04 (0.03) 10 Type of Pain Acute −.40 (0.02) 43 191.69*** Chronic .00 (0.02) 48 Mixed −.09 (0.02) 5 Pain Condition Arthritis/musculoskeletal pain .20 (0.04) 10 163.80*** Burn .02 (0.09) 3 Cancer −.02 (0.02) 35 Surgery or procedure −.32 (0.03) 15 Laboratory −.51 (0.06) 12 Assessment Method Faces Pain Scale −.03 (0.05) 8 54.56*** NRS/Likert −.12 (0.02) 39 VAS −.22 (0.02) 45 Other, mixed .14 (0.05) 11 Location of assessment Inpatient −.05 (0.03) 28 93.52*** Outpatient −.03 (0.02) 31 Laboratory −.64 (0.06) 11 Mixed .09 (0.05) 3 Moderator Categories Effect size da (SE) Number of effect sizes Fixed effects comparisons For pain assessment accuracy reported as paired comparisons Year <1996 −.18 (0.03) 23 2.28 ≥1996 −.12 (0.01) 80 Country Asia/Middle East −.08 (0.04) 9 38.09*** Europe −.21 (0.02) 37 USA/Canada −.10 (0.02) 54 Other .19 (0.08) 3 Patient Gender <50% male −.02 (0.02) 46 35.87*** ≥50% male −.17 (0.02) 41 Patient Age Group Children −.09 (0.05) 8 89.68*** Adults −.08 (0.01) 62 Older adults −.22 (0.07) 9 Mixed −.44 (0.04) 24 Observer type Informal caregivers .17 (0.02) 34 396.87*** Nurses −.21 (0.03) 32 Physicians −.41 (0.04) 20 Other healthcare providers −.24 (0.02) 6 Other observers −.64 (0.06) 11 Observer Gender <50% male .02 (0.02) 52 .09 ≥50 % male .04 (0.03) 10 Type of Pain Acute −.40 (0.02) 43 191.69*** Chronic .00 (0.02) 48 Mixed −.09 (0.02) 5 Pain Condition Arthritis/musculoskeletal pain .20 (0.04) 10 163.80*** Burn .02 (0.09) 3 Cancer −.02 (0.02) 35 Surgery or procedure −.32 (0.03) 15 Laboratory −.51 (0.06) 12 Assessment Method Faces Pain Scale −.03 (0.05) 8 54.56*** NRS/Likert −.12 (0.02) 39 VAS −.22 (0.02) 45 Other, mixed .14 (0.05) 11 Location of assessment Inpatient −.05 (0.03) 28 93.52*** Outpatient −.03 (0.02) 31 Laboratory −.64 (0.06) 11 Mixed .09 (0.05) 3 NRS numeric rating scale; SE standard error; VAS visual analogue scale. ad is standard difference in means. *p < .05; **p < .01; ***p < .001. View Large Table 5 Moderators of paired comparison pain assessment accuracy Moderator Categories Effect size da (SE) Number of effect sizes Fixed effects comparisons For pain assessment accuracy reported as paired comparisons Year <1996 −.18 (0.03) 23 2.28 ≥1996 −.12 (0.01) 80 Country Asia/Middle East −.08 (0.04) 9 38.09*** Europe −.21 (0.02) 37 USA/Canada −.10 (0.02) 54 Other .19 (0.08) 3 Patient Gender <50% male −.02 (0.02) 46 35.87*** ≥50% male −.17 (0.02) 41 Patient Age Group Children −.09 (0.05) 8 89.68*** Adults −.08 (0.01) 62 Older adults −.22 (0.07) 9 Mixed −.44 (0.04) 24 Observer type Informal caregivers .17 (0.02) 34 396.87*** Nurses −.21 (0.03) 32 Physicians −.41 (0.04) 20 Other healthcare providers −.24 (0.02) 6 Other observers −.64 (0.06) 11 Observer Gender <50% male .02 (0.02) 52 .09 ≥50 % male .04 (0.03) 10 Type of Pain Acute −.40 (0.02) 43 191.69*** Chronic .00 (0.02) 48 Mixed −.09 (0.02) 5 Pain Condition Arthritis/musculoskeletal pain .20 (0.04) 10 163.80*** Burn .02 (0.09) 3 Cancer −.02 (0.02) 35 Surgery or procedure −.32 (0.03) 15 Laboratory −.51 (0.06) 12 Assessment Method Faces Pain Scale −.03 (0.05) 8 54.56*** NRS/Likert −.12 (0.02) 39 VAS −.22 (0.02) 45 Other, mixed .14 (0.05) 11 Location of assessment Inpatient −.05 (0.03) 28 93.52*** Outpatient −.03 (0.02) 31 Laboratory −.64 (0.06) 11 Mixed .09 (0.05) 3 Moderator Categories Effect size da (SE) Number of effect sizes Fixed effects comparisons For pain assessment accuracy reported as paired comparisons Year <1996 −.18 (0.03) 23 2.28 ≥1996 −.12 (0.01) 80 Country Asia/Middle East −.08 (0.04) 9 38.09*** Europe −.21 (0.02) 37 USA/Canada −.10 (0.02) 54 Other .19 (0.08) 3 Patient Gender <50% male −.02 (0.02) 46 35.87*** ≥50% male −.17 (0.02) 41 Patient Age Group Children −.09 (0.05) 8 89.68*** Adults −.08 (0.01) 62 Older adults −.22 (0.07) 9 Mixed −.44 (0.04) 24 Observer type Informal caregivers .17 (0.02) 34 396.87*** Nurses −.21 (0.03) 32 Physicians −.41 (0.04) 20 Other healthcare providers −.24 (0.02) 6 Other observers −.64 (0.06) 11 Observer Gender <50% male .02 (0.02) 52 .09 ≥50 % male .04 (0.03) 10 Type of Pain Acute −.40 (0.02) 43 191.69*** Chronic .00 (0.02) 48 Mixed −.09 (0.02) 5 Pain Condition Arthritis/musculoskeletal pain .20 (0.04) 10 163.80*** Burn .02 (0.09) 3 Cancer −.02 (0.02) 35 Surgery or procedure −.32 (0.03) 15 Laboratory −.51 (0.06) 12 Assessment Method Faces Pain Scale −.03 (0.05) 8 54.56*** NRS/Likert −.12 (0.02) 39 VAS −.22 (0.02) 45 Other, mixed .14 (0.05) 11 Location of assessment Inpatient −.05 (0.03) 28 93.52*** Outpatient −.03 (0.02) 31 Laboratory −.64 (0.06) 11 Mixed .09 (0.05) 3 NRS numeric rating scale; SE standard error; VAS visual analogue scale. ad is standard difference in means. *p < .05; **p < .01; ***p < .001. View Large Characteristics of the patient: paired comparison The number of patients in the studies ranged from 3 to 1923 (M = 128.59, SD = 257.75). Participant characteristics including age group (Q = 89.68, p < .001) and gender (Q = 35.87, p < .001) significantly impacted pain assessment accuracy. There was significantly more underestimation of patient pain when the majority of patients in the study were male, and when assessing pain in older adults or in mixed patient samples. Characteristics of the observer: paired comparison The number of observers in the study ranged from 5 to 1928 (M = 106.04, SD = 247.16). Observer type was a significant moderator of pain assessment accuracy (Q = 396.87, p < .001). Caregivers significantly overestimated patient pain (d = .17) while all other observer types (nurses, physicians, other healthcare providers and other observers) significantly underestimated patient pain. Physicians and other observers showed the most underestimation of patient pain (d = −.41 and d = −.59). Clinical experience (k = 14) and observer gender were not significant moderators of pain assessment accuracy in the paired comparison meta-analysis (clinical experience: β = -0.01, 95% CI: −0.06, 0.03; observer gender: Q = 0.09, p = .76). Characteristics of the pain and assessment: paired comparison There was significantly more underestimation of pain when the pain being assessed was acute (Q = 191.69, p < .001). Chronic pain did not show significant over or underestimation of pain. Pain condition significantly moderated accuracy (Q = 163.80, p < .001). There was significant overestimation by observers for arthritis and musculoskeletal pain and significant underestimation for pain related to surgeries, other procedures, or experimentally-induced pain. Cancer and burn pain assessment did not show significant over or underestimation. More underestimation of pain occurred in laboratory settings (Q = 93.52, p < .001). Underestimation of pain by observers occurred for the NRS and VAS, but not for other pain assessment methods (Q = 54.56, p < .001). In terms of clinical practice, it may be relevant for providers and caregivers to know, on average, how many points on a pain assessment scale they under or overestimated pain. When just examining the studies that allowed for an unstandardized mean difference comparison for the NRS and VAS, results were consistent with the overall paired comparison analysis reported above. For the unstandardized studies using the NRS (0–10 Likert scale), nurses (k = 8) showed an average underestimation of about 3/4 of a point or 6.55% underestimation (M = −0.72, SD = 0.98), studies with physicians (k = 2) showed an average underestimation of about 1/3 of a point or 3.18% underestimation (M = −0.35, SD = 0.49), while studies with caregivers (k = 11) showed an average overestimation of 1/3 of a point or 2.45% overstimation (M = 0.27, SD = 0.22). For the unstandardized studies using the VAS (0–100 scale), nurses (k = 7) showed an average underestimation of 7 points or 6.93% underestimation (M = −7.28, SD = 20.55), studies with physicians (k = 9) showed an average underestimation of 3.5 points or 3.47% underestimation (M = −3.50, SD = 10.27), while studies with caregivers (k = 3) showed an average overestimation of close to 2 points or 1.98% overestimation (M = −1.79, SD = 5.04). Pain Assessment Accuracy Among Parents No studies to date have examined overall accuracy for parent–child dyads, therefore, a post hoc analysis examined the effect sizes for the subset of these eligible studies in the correlational meta (k = 11) and paired comparison meta (k = 4). In these studies, the average age was approximately 6 years old and the majority of parents who participated were mothers. Because there were so few effect sizes, we could not conduct meaningful moderator analyses but present the overall effects. In studies that examined correlational pain assessment accuracy for parents as the observers and their children as the patients in pain, the random effects mean effect size was r = .54 (95% CI: 0.41, 0.65) and the fixed, weighted mean effect size was r = .53 (95% CI: 0.48, 0.58), both higher than the overall mean effect sizes presented for all caregivers (Table 3). The significant combined Z = 17.63 (p < .001) indicates that parent observers were significantly better than chance (chance r = 0) at assessing their child’s pain. In studies that examined paired comparison pain assessment accuracy for parents as the observers and their children as the patients in pain, the random mean effect size was d = .007 (95% CI: −0.23, 0.25) and the fixed, weighted mean effect size was d = .007 (95% CI: −0.11, 0.12). The combined Z = 0.07 (p = .94) was nonsignificant, indicating that parents did not significantly under or overestimate their child’s pain while other caregivers (spouses, adult children, friends, etc.) did significantly overestimate pain (see Table 5). Discussion The correlational pain assessment meta-analysis showed that in general, observers were significantly better than chance at assessing pain; however, the paired comparison meta-analysis showed that observers significantly underestimated patients’ pain. Of the studies that report unstandardized mean difference results, there was consistent underestimation for nurses (6.74% underestimation) and physicians (3.33% underestimation) and consistent overestimation for caregivers (2.22% overestimation). This type of underestimation by healthcare providers may result in significant differences in clinical patient care (e.g., not receiving appropriate pain medication [38]). The finding that accuracy (correlational or paired comparison) did not improve across study year is consistent with analyses showing that measuring pain as the 5th vital sign does not necessarily improve quality of pain care and management [39]. There was lower correlational accuracy and a tendency to underestimate patients’ pain more in Canada and the USA compared to Europe and Asia. There are cultural differences in pain expression and the clinical experience of pain that could account for the locational differences in assessment accuracy [40]. The highest correlational accuracy occurred when observers were assessing pain in children compared to when they were assessing pain in adults or older adults. In the paired comparison moderator analysis, older patients were more likely to have their pain underestimated. Children may be less inhibited and express pain more spontaneously and freely thus making judgments by observers more accurate. It may also be that perceiving a child in pain is less frequent than an older adult or adult in pain thus making the salience of the event stronger, increasing attention to the patient and making judgments more accurate. Most studies of children also employ the Faces Pain Scale (as opposed to a NRS or VAS), thus potentially explaining the higher correlational accuracy seen for this pain assessment method. Caregivers showed the highest accuracy followed by nurses and physicians with the lowest accuracy among other healthcare providers and other observers. A substantially different pattern emerged for the paired comparison meta-analysis by observer type. Caregivers were the only group to significantly overestimate patients’ pain while all other observers (nurses, physicians, other healthcare providers and other observers) underestimated patients’ pain. Among the types of caregivers, studies with parents showed the highest accuracy at assessing their child’s pain, as there was almost no difference in parent’s assessment of pain and their child’s self-report of pain. This finding is particularly relevant in clinical practice for using parents as proxies for their child’s pain report when in pain or undergoing a painful procedure. We do caution the interpretation of these results, as there was a limited group of studies that examined pain assessment accuracy in parent–child dyads. Future research should examine pain assessment accuracy in parent–child dyads and potential moderators of accuracy. In studies with other family members, typically with older adult populations, caregivers tended to overestimate a loved one’s pain. Physicians and strangers underestimated pain more than others, suggesting that an established personal relationship may be important for understanding fluctuations in pain intensity, correlational pain assessment accuracy. However, this personal relationship may cause caregivers of adult patients to overestimate a loved one’s pain. Taken together, these findings argue for an interdisciplinary team approach with an established patient–provider relationship in order to maximize the accuracy of pain estimates. In the paired comparison meta-analysis, no observer group judged patient pain accurately, except when only examining studies with parents. This finding calls for the need to train providers and caregivers on assessing pain at the level experienced by the patient. Moreover, results suggest the benefit of a multimethod assessment of pain using multiple observers including, patients, caregivers, and healthcare providers and additionally, integrating the Faces Pain Scale assessment. Clinical experience had no effect on correlational pain assessment accuracy or the paired comparison accuracy among physicians, nurses, and other healthcare providers; however, strong conclusions are limited because few studies included in the meta-analyses reported levels of clinical experience. We were not able to code for whether providers in these studies had training specific to patient pain assessment. This type of dedicated training has been shown to increase accuracy in previous studies [41]. We caution generalizations from any of the provider type analyses because we did not have enough information about provider training or type other than broad categories. Future research should gather this information in studies on pain assessment to understand how accuracy varies by clinical specialization (e.g., emergency department vs. family practice). Observer gender impacted correlational pain assessment accuracy such that studies with a higher proportion of men than women were more accurate than studies with a higher proportion of women than men. This finding is supported by the theory and research showing that although women are more accurate when judging thoughts, feelings, and emotions compared to men, men may be more accurate when judging others and their own visceral experiences [7, 42–44]. Observer gender did not impact the paired comparison pain assessment accuracy, that is men and women equally over and underestimated patient pain and patient gender had no impact on correlational pain assessment accuracy. There was no difference in correlational accuracy when judging acute versus chronic pain. This finding is in line with research showing no pain expression differences in acute versus chronic pain [32]. However, there was significant underestimation of acute pain, while chronic pain was neither under or overestimated. These findings are particularly salient given that acute pain control is associated with better patient outcomes and a reduction in chronic pain development [45]. The International Association for the Study of Pain [46] has declared timely access to pain control as a major public health issue and basic human right and that delaying pain treatment can result in escalating pain, depression, and decreased quality of life [47, 48]. Higher correlational accuracy was found for burn, cancer, and surgical pain compared to arthritis or musculoskeletal pain. Arthritis and musculoskeletal pain also was significantly overestimated while surgical, procedure-based and laboratory-based pain was significantly underestimated. Neither cancer nor burn pain was significantly over- or underestimated. Medical educators and researchers should be aware of these potential differences in pain assessment accuracy across pain type. There are several limitations of the present meta-analysis due to limitations in the reporting of previous literature. None of the studies examined what specific cues providers used when making their assessments of pain (e.g., patients’ nonverbal and verbal behavior, patients’ injury/disease, or physiological indicators) and whether this influenced accuracy. Nonverbal behaviors are powerful indicators of the pain experience and for infants, appear to be the most consistent expression of pain [49]. Providers may be most accurate at assessing pain when they focus on nonverbal expressions of pain because nonverbal behaviors may be more spontaneous and less under the volitional control of the sufferer. Future research should examine providers’ and caregivers’ pain assessment accuracy for nonverbal patient populations (e.g., patients with dementia, Alzheimer’s, or older adults with limited communication abilities). In the present meta-analysis, only three studies met inclusion criteria that specified nonverbal or dementia patients. In addition, we focused exclusively on accuracy as defined as the direct comparison between providers’ and caregivers’ assessments of patients’ pain and patients’ self-reported pain (the criteria), given that this is how the literature (and clinical care) measures patient pain. However, future research on observers’ pain assessment accuracy would benefit from exploring other criteria in addition to patients’ self-report, especially in populations where self-reports may not be the most valid measure of pain. Coding systems and behavioral checklists have been designed to measure pain using nonverbal behaviors in infants and for patients with cognitive impairments. In addition, observers’ pain assessments could be compared to patients’ physiological responses to pain, fMRI responses to pain, or caregiver/proxy reports of pain. Another limitation involves the use of different pain assessment scales. These scales ranged from 6 items (e.g., Faces Pain Scale: 0, 2, 4, 6, 8, 10) to 101 items (e.g., VAS 0–100). As the moderator results suggested, observers had higher pain assessment accuracy in studies that used a version of the Faces Pain Scale compared to studies that used another type of scale. While the pain assessment scale was often confounded with patient age (e.g., studies that used the Faces Pain Scale were more likely to be used with children), it is difficult to know whether to attribute this higher correlational accuracy to the scale or the patient population. However, research on scales suggests that correlations are likely to be stronger and more accurate with a 100-point rating scale compared to a 6-point rating scale [50]. Therefore, we believe that observers were able to perceive pain more accurately in children and that the Faces Pain Scale did not account for these differences. In clinical practice and research, it would be helpful for patients, caregivers and providers if one type of pain scale was used consistently, rather than each medical center or healthcare system selecting their own pain assessment tool. This type of consistency would allow for a more consistent comparison of patient pain assessments over time as electronic medical records and medical record sharing are becoming more common. Further, different types of providers may want to track the same patient’s pain over time or compare two patients with the same type of disease in terms of their pain. In addition, using the same pain assessment tool across clinical and research practice would allow for pain to be used as an outcome or quality indicator (e.g., to assess how well various emergency departments are treating pain). Implications Results from these two meta-analyses examining two different approaches to pain assessment accuracy provide overall, generalizable estimates of accuracy for researchers, providers, and educators involved in pain assessment research, clinical care, and training. It is important to acknowledge that even when providers have clinical or assessment information about patients, bias can interfere with how patient pain is managed. In the current meta-analysis, providers did not have access to patients’ self-reports of pain prior to making inferences about pain, however, research has established that healthcare providers’ implicit and explicit bias affects how they treat and assess pain [51, 52]. In considering the application of these meta-analytic results, it is first important to address cultural differences across studies in the USA/Canada versus other locales. Unfortunately, other markers of culture such as race and ethnicity (of both the patient and observers) were not examined due to lack of reporting in the studies, but could also have a unique impact. Researchers, clinicians, and medical educators conducting pain assessment research, care, and training should also be aware of the potential for providers to underestimate pain of males and older adults. Additionally, acute pain is likely to be underestimated, despite literature that suggests that the most effective tool for reducing the development of chronic pain is effective acute pain management [53–55]. Factors such as the publication year and provider clinical experience did not have an impact on accuracy of pain assessment. These findings are concerning in the context of pain management and opioid addiction. It does not appear that the increased focus on pain and addiction in medicine in the past 20 years has led to increased accuracy in pain assessment. Clinicians face a difficult dialectic of needing to accurately assess and control acute pain, without creating dependency on opioid medications. This underscores the importance of interdisciplinary pain centers and possibly the interdisciplinary assessment of patient pain, in which patients, caregivers, and the healthcare team assess pain together. The results across multiple studies suggest data-driven processes for clinics attempting to accurately assess and appropriately manage pain. Multiple observers in an interdisciplinary team environment can assess pain with the possibility of averaging the pain estimates across observers. Indeed, caregivers showed the highest accuracy, but tended to overestimate. Physicians, on the other hand, were more likely to underestimate patient pain. Thus, by weighting both ratings into a pain assessment composite, more accurate assessments and treatment may be possible. Acknowledgments We would like to thank research assistants, Tobias Perschel and Ayesha Ludhani for their help in data entry. We would also like to thank Dr. Judith Hall for her meta-analysis mentorship. The work and research assistants were partially supported by a Bentley University internal faculty development grant to Danielle Blanch-Hartigan. The views, opinions, and content of this publication are those of the authors and do not necessarily reflect the views, opinions, or policies of the Department of Veterans Affairs or the United States Government. Compliance with Ethical Standards Statements Funding The work and research assistants were partially supported by a Bentley University internal faculty development grant to Danielle Blanch-Hartigan. The views, opinions, and content of this publication are those of the authors and do not necessarily reflect the views, opinions, or policies of the Department of Veterans Affairs or the United States Government. Conflict of Interest The authors report no conflicts of interest. Appendix Table A1 Moderators present for correlational pain assessment meta-analysis First author Country Patient gender Patient age group Observer gender Type of pain Pain condition Location of assessment Akin [56] Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Allen [30] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Baxt [31] USA/Canada Mostly male Children Acute Outpatient Bergh [57] Europe Older adults only (65+) Chronic Arthritis/ musculoskeletal Inpatient Blomqvist [58] Europe Mostly female Older adults only (65+) Both Outpatient Broberger [59] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Europe Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Chambers [60] USA/Canada Mostly male Children Mostly female Acute Surgery or procedure Inpatient Choiniere [61] USA/Canada Mostly male Acute Outpatient Clipp [62] USA/Canada Mostly male Adults (18+) Mostly male Chronic Cancer Outpatient Colwell [63] USA/Canada Mostly female Children Acute Surgery or procedure Inpatient Cremeans-Smith [64] USA/Canada Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient USA/Canada Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient Dawber [65] Europe Mostly female Older adults only (65+) Mostly female Chronic Cancer Inpatient Europe Mostly female Older adults only (65+) Mostly female Chronic Cancer Inpatient de Bock [66] Europe Mostly female Adults (18+) Mostly male Chronic Arthritis/ musculoskeletal Outpatient Doherty [67] Europe Mostly female Children Chronic Arthritis/ musculoskeletal Outpatient Drayer [68] USA/Canada Both Everett [69] USA/Canada Mostly male Mostly female Acute Burn Outpatient Forrest [70] Europe Acute Fridh [71] USA/Canada Mostly female Adults (18+) Mostly female Acute Pregnancy/labor Inpatient Gil [72] Asia/Middle East Mostly male Adults (18+) Chronic Cancer Inpatient Harrison [73] Asia/Middle East Hays [74] USA/Canada Mostly male Adults (18+) Mostly female Chronic Outpatient Heikkinen [75] Europe Adults (18+) Heuss [76] Europe Mostly male Adults (18+) Acute Cancer Outpatient Europe Mostly male Adults (18+) Acute Cancer Outpatient Higginson [77] Europe Mostly male Older adults only (65+) Mostly female Chronic Cancer Hodgkins [78] USA/Canada Mostly female Adults (18+) Horgas [79] USA/Canada Mostly female Adults (18+) Mostly female Both Outpatient Idvall [80] USA/Canada Mostly female Mostly female Acute Surgery or procedure Kelly [81] Other Children Mostly female Acute Inpatient Kristjanson [82] Other Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Lautenbacher [28] Europe Adults (18+) Mostly female Acute Lab-based procedure Outpatient Europe Adults (18+) Mostly female Acute Lab-based procedure Outpatient Lin [83] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Lobchuk [84] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Lobchuk [20] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Madison [85] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Maguire [86] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Outpatient Manne [87] USA/Canada Mostly male Children Mostly female Chronic Cancer Outpatient USA/Canada Mostly male Children Chronic Cancer Outpatient Mantyselka [5] Europe Mostly female Both Arthritis/musculoskeletal Outpatient McKinley [88] Other Mostly female Adults (18+) Inpatient McMillan [89] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient McPherson [90] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Miller [91] USA/Canada Children Mostly female Acute Surgery or procedure Inpatient USA/Canada Children Acute Surgery or procedure Inpatient O’Brien [92] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Oi-Ling [93] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Paice [94] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient Perreault [95] USA/Canada Mostly female Adults (18+) Mostly female Chronic Arthritis/ musculoskeletal Outpatient Powers [96] USA/Canada Mostly male Children Acute Surgery or procedure Inpatient Prkachin [97] USA/Canada Mostly female Acute Prkachin [98] USA/Canada Acute USA/Canada Mostly male Acute Laboratory Redinbaugh [99] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Resnizky [100] Asia/Middle East Mostly female Older adults only (65+) Mostly female Chronic Inpatient Rhondali [101] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Ruben [7] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Ruben [102] USA/Canada Mostly female Adults (18+) Mostly female Acute Lab-based procedure Laboratory Salmon [103] Europe Mostly male Adults (18+) Mostly female Acute Surgery or procedure Inpatient Schneider [104] USA/Canada Children Mostly female Acute Surgery or procedure Outpatient USA/Canada Children Acute Surgery or procedure Outpatient Shega [105] USA/Canada Mostly female Older adults only (65+) Mostly female Chronic Silveira [106] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Outpatient Singer [107] USA/Canada Mostly male Children Mostly female Acute Outpatient Singer [108] USA/Canada Mostly male Children Mostly male Acute Outpatient USA/Canada Mostly male Acute Outpatient Sjöström [109] Europe Acute Surgery or procedure Inpatient Europe Acute Surgery or procedure Inpatient Sneeuw [110] Other Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Sneeuw [111] Europe Mostly female Adults (18+) Mostly male Chronic Cancer Sneeuw [112] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Europe Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Europe Mostly male Adults (18+) Mostly male Chronic Cancer Inpatient Stephenson [113] USA/Canada Mostly male Adults (18+) Mostly female Acute Surgery or procedure Inpatient St-Laurent-Gagnon [114] USA/Canada Mostly female Children Mostly female Acute Surgery or procedure Outpatient Suarez-Almazor [115] USA/Canada Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient Sutherland [116] USA/Canada Adults (18+) Acute van Herk [117] Europe Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient Europe Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient Van Der Does [118] Europe Mostly male Mostly female Acute Burn Vervoort [119] Europe Mostly male Children Mostly female Acute Lab-based procedure Walkenstein [120] USA/Canada Mostly male Acute Burn Weiner [121] USA/Canada Mostly male Adults (18+) Mostly female Chronic Arthritis/musculoskeletal Inpatient USA/Canada Mostly male Adults (18+) Mostly female Chronic Arthritis/musculoskeletal Inpatient Wennman-Larsen [122] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Yesilbalkan [123] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Zalon [124] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient Zhukovsky [125] USA/Canada Mostly male Children Mostly female Chronic Cancer Outpatient USA/Canada Mostly male Children Chronic Cancer Outpatient First author Country Patient gender Patient age group Observer gender Type of pain Pain condition Location of assessment Akin [56] Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Allen [30] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Baxt [31] USA/Canada Mostly male Children Acute Outpatient Bergh [57] Europe Older adults only (65+) Chronic Arthritis/ musculoskeletal Inpatient Blomqvist [58] Europe Mostly female Older adults only (65+) Both Outpatient Broberger [59] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Europe Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Chambers [60] USA/Canada Mostly male Children Mostly female Acute Surgery or procedure Inpatient Choiniere [61] USA/Canada Mostly male Acute Outpatient Clipp [62] USA/Canada Mostly male Adults (18+) Mostly male Chronic Cancer Outpatient Colwell [63] USA/Canada Mostly female Children Acute Surgery or procedure Inpatient Cremeans-Smith [64] USA/Canada Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient USA/Canada Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient Dawber [65] Europe Mostly female Older adults only (65+) Mostly female Chronic Cancer Inpatient Europe Mostly female Older adults only (65+) Mostly female Chronic Cancer Inpatient de Bock [66] Europe Mostly female Adults (18+) Mostly male Chronic Arthritis/ musculoskeletal Outpatient Doherty [67] Europe Mostly female Children Chronic Arthritis/ musculoskeletal Outpatient Drayer [68] USA/Canada Both Everett [69] USA/Canada Mostly male Mostly female Acute Burn Outpatient Forrest [70] Europe Acute Fridh [71] USA/Canada Mostly female Adults (18+) Mostly female Acute Pregnancy/labor Inpatient Gil [72] Asia/Middle East Mostly male Adults (18+) Chronic Cancer Inpatient Harrison [73] Asia/Middle East Hays [74] USA/Canada Mostly male Adults (18+) Mostly female Chronic Outpatient Heikkinen [75] Europe Adults (18+) Heuss [76] Europe Mostly male Adults (18+) Acute Cancer Outpatient Europe Mostly male Adults (18+) Acute Cancer Outpatient Higginson [77] Europe Mostly male Older adults only (65+) Mostly female Chronic Cancer Hodgkins [78] USA/Canada Mostly female Adults (18+) Horgas [79] USA/Canada Mostly female Adults (18+) Mostly female Both Outpatient Idvall [80] USA/Canada Mostly female Mostly female Acute Surgery or procedure Kelly [81] Other Children Mostly female Acute Inpatient Kristjanson [82] Other Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Lautenbacher [28] Europe Adults (18+) Mostly female Acute Lab-based procedure Outpatient Europe Adults (18+) Mostly female Acute Lab-based procedure Outpatient Lin [83] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Lobchuk [84] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Lobchuk [20] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Madison [85] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Maguire [86] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Outpatient Manne [87] USA/Canada Mostly male Children Mostly female Chronic Cancer Outpatient USA/Canada Mostly male Children Chronic Cancer Outpatient Mantyselka [5] Europe Mostly female Both Arthritis/musculoskeletal Outpatient McKinley [88] Other Mostly female Adults (18+) Inpatient McMillan [89] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient McPherson [90] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Miller [91] USA/Canada Children Mostly female Acute Surgery or procedure Inpatient USA/Canada Children Acute Surgery or procedure Inpatient O’Brien [92] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Oi-Ling [93] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Paice [94] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient Perreault [95] USA/Canada Mostly female Adults (18+) Mostly female Chronic Arthritis/ musculoskeletal Outpatient Powers [96] USA/Canada Mostly male Children Acute Surgery or procedure Inpatient Prkachin [97] USA/Canada Mostly female Acute Prkachin [98] USA/Canada Acute USA/Canada Mostly male Acute Laboratory Redinbaugh [99] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Resnizky [100] Asia/Middle East Mostly female Older adults only (65+) Mostly female Chronic Inpatient Rhondali [101] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Ruben [7] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Ruben [102] USA/Canada Mostly female Adults (18+) Mostly female Acute Lab-based procedure Laboratory Salmon [103] Europe Mostly male Adults (18+) Mostly female Acute Surgery or procedure Inpatient Schneider [104] USA/Canada Children Mostly female Acute Surgery or procedure Outpatient USA/Canada Children Acute Surgery or procedure Outpatient Shega [105] USA/Canada Mostly female Older adults only (65+) Mostly female Chronic Silveira [106] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Outpatient Singer [107] USA/Canada Mostly male Children Mostly female Acute Outpatient Singer [108] USA/Canada Mostly male Children Mostly male Acute Outpatient USA/Canada Mostly male Acute Outpatient Sjöström [109] Europe Acute Surgery or procedure Inpatient Europe Acute Surgery or procedure Inpatient Sneeuw [110] Other Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Sneeuw [111] Europe Mostly female Adults (18+) Mostly male Chronic Cancer Sneeuw [112] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Europe Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Europe Mostly male Adults (18+) Mostly male Chronic Cancer Inpatient Stephenson [113] USA/Canada Mostly male Adults (18+) Mostly female Acute Surgery or procedure Inpatient St-Laurent-Gagnon [114] USA/Canada Mostly female Children Mostly female Acute Surgery or procedure Outpatient Suarez-Almazor [115] USA/Canada Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient Sutherland [116] USA/Canada Adults (18+) Acute van Herk [117] Europe Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient Europe Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient Van Der Does [118] Europe Mostly male Mostly female Acute Burn Vervoort [119] Europe Mostly male Children Mostly female Acute Lab-based procedure Walkenstein [120] USA/Canada Mostly male Acute Burn Weiner [121] USA/Canada Mostly male Adults (18+) Mostly female Chronic Arthritis/musculoskeletal Inpatient USA/Canada Mostly male Adults (18+) Mostly female Chronic Arthritis/musculoskeletal Inpatient Wennman-Larsen [122] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Yesilbalkan [123] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Zalon [124] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient Zhukovsky [125] USA/Canada Mostly male Children Mostly female Chronic Cancer Outpatient USA/Canada Mostly male Children Chronic Cancer Outpatient For country “other” is Australia or multiple countries. View Large Table A1 Moderators present for correlational pain assessment meta-analysis First author Country Patient gender Patient age group Observer gender Type of pain Pain condition Location of assessment Akin [56] Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Allen [30] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Baxt [31] USA/Canada Mostly male Children Acute Outpatient Bergh [57] Europe Older adults only (65+) Chronic Arthritis/ musculoskeletal Inpatient Blomqvist [58] Europe Mostly female Older adults only (65+) Both Outpatient Broberger [59] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Europe Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Chambers [60] USA/Canada Mostly male Children Mostly female Acute Surgery or procedure Inpatient Choiniere [61] USA/Canada Mostly male Acute Outpatient Clipp [62] USA/Canada Mostly male Adults (18+) Mostly male Chronic Cancer Outpatient Colwell [63] USA/Canada Mostly female Children Acute Surgery or procedure Inpatient Cremeans-Smith [64] USA/Canada Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient USA/Canada Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient Dawber [65] Europe Mostly female Older adults only (65+) Mostly female Chronic Cancer Inpatient Europe Mostly female Older adults only (65+) Mostly female Chronic Cancer Inpatient de Bock [66] Europe Mostly female Adults (18+) Mostly male Chronic Arthritis/ musculoskeletal Outpatient Doherty [67] Europe Mostly female Children Chronic Arthritis/ musculoskeletal Outpatient Drayer [68] USA/Canada Both Everett [69] USA/Canada Mostly male Mostly female Acute Burn Outpatient Forrest [70] Europe Acute Fridh [71] USA/Canada Mostly female Adults (18+) Mostly female Acute Pregnancy/labor Inpatient Gil [72] Asia/Middle East Mostly male Adults (18+) Chronic Cancer Inpatient Harrison [73] Asia/Middle East Hays [74] USA/Canada Mostly male Adults (18+) Mostly female Chronic Outpatient Heikkinen [75] Europe Adults (18+) Heuss [76] Europe Mostly male Adults (18+) Acute Cancer Outpatient Europe Mostly male Adults (18+) Acute Cancer Outpatient Higginson [77] Europe Mostly male Older adults only (65+) Mostly female Chronic Cancer Hodgkins [78] USA/Canada Mostly female Adults (18+) Horgas [79] USA/Canada Mostly female Adults (18+) Mostly female Both Outpatient Idvall [80] USA/Canada Mostly female Mostly female Acute Surgery or procedure Kelly [81] Other Children Mostly female Acute Inpatient Kristjanson [82] Other Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Lautenbacher [28] Europe Adults (18+) Mostly female Acute Lab-based procedure Outpatient Europe Adults (18+) Mostly female Acute Lab-based procedure Outpatient Lin [83] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Lobchuk [84] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Lobchuk [20] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Madison [85] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Maguire [86] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Outpatient Manne [87] USA/Canada Mostly male Children Mostly female Chronic Cancer Outpatient USA/Canada Mostly male Children Chronic Cancer Outpatient Mantyselka [5] Europe Mostly female Both Arthritis/musculoskeletal Outpatient McKinley [88] Other Mostly female Adults (18+) Inpatient McMillan [89] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient McPherson [90] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Miller [91] USA/Canada Children Mostly female Acute Surgery or procedure Inpatient USA/Canada Children Acute Surgery or procedure Inpatient O’Brien [92] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Oi-Ling [93] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Paice [94] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient Perreault [95] USA/Canada Mostly female Adults (18+) Mostly female Chronic Arthritis/ musculoskeletal Outpatient Powers [96] USA/Canada Mostly male Children Acute Surgery or procedure Inpatient Prkachin [97] USA/Canada Mostly female Acute Prkachin [98] USA/Canada Acute USA/Canada Mostly male Acute Laboratory Redinbaugh [99] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Resnizky [100] Asia/Middle East Mostly female Older adults only (65+) Mostly female Chronic Inpatient Rhondali [101] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Ruben [7] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Ruben [102] USA/Canada Mostly female Adults (18+) Mostly female Acute Lab-based procedure Laboratory Salmon [103] Europe Mostly male Adults (18+) Mostly female Acute Surgery or procedure Inpatient Schneider [104] USA/Canada Children Mostly female Acute Surgery or procedure Outpatient USA/Canada Children Acute Surgery or procedure Outpatient Shega [105] USA/Canada Mostly female Older adults only (65+) Mostly female Chronic Silveira [106] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Outpatient Singer [107] USA/Canada Mostly male Children Mostly female Acute Outpatient Singer [108] USA/Canada Mostly male Children Mostly male Acute Outpatient USA/Canada Mostly male Acute Outpatient Sjöström [109] Europe Acute Surgery or procedure Inpatient Europe Acute Surgery or procedure Inpatient Sneeuw [110] Other Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Sneeuw [111] Europe Mostly female Adults (18+) Mostly male Chronic Cancer Sneeuw [112] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Europe Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Europe Mostly male Adults (18+) Mostly male Chronic Cancer Inpatient Stephenson [113] USA/Canada Mostly male Adults (18+) Mostly female Acute Surgery or procedure Inpatient St-Laurent-Gagnon [114] USA/Canada Mostly female Children Mostly female Acute Surgery or procedure Outpatient Suarez-Almazor [115] USA/Canada Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient Sutherland [116] USA/Canada Adults (18+) Acute van Herk [117] Europe Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient Europe Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient Van Der Does [118] Europe Mostly male Mostly female Acute Burn Vervoort [119] Europe Mostly male Children Mostly female Acute Lab-based procedure Walkenstein [120] USA/Canada Mostly male Acute Burn Weiner [121] USA/Canada Mostly male Adults (18+) Mostly female Chronic Arthritis/musculoskeletal Inpatient USA/Canada Mostly male Adults (18+) Mostly female Chronic Arthritis/musculoskeletal Inpatient Wennman-Larsen [122] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Yesilbalkan [123] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Zalon [124] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient Zhukovsky [125] USA/Canada Mostly male Children Mostly female Chronic Cancer Outpatient USA/Canada Mostly male Children Chronic Cancer Outpatient First author Country Patient gender Patient age group Observer gender Type of pain Pain condition Location of assessment Akin [56] Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Allen [30] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Baxt [31] USA/Canada Mostly male Children Acute Outpatient Bergh [57] Europe Older adults only (65+) Chronic Arthritis/ musculoskeletal Inpatient Blomqvist [58] Europe Mostly female Older adults only (65+) Both Outpatient Broberger [59] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Europe Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Chambers [60] USA/Canada Mostly male Children Mostly female Acute Surgery or procedure Inpatient Choiniere [61] USA/Canada Mostly male Acute Outpatient Clipp [62] USA/Canada Mostly male Adults (18+) Mostly male Chronic Cancer Outpatient Colwell [63] USA/Canada Mostly female Children Acute Surgery or procedure Inpatient Cremeans-Smith [64] USA/Canada Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient USA/Canada Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient Dawber [65] Europe Mostly female Older adults only (65+) Mostly female Chronic Cancer Inpatient Europe Mostly female Older adults only (65+) Mostly female Chronic Cancer Inpatient de Bock [66] Europe Mostly female Adults (18+) Mostly male Chronic Arthritis/ musculoskeletal Outpatient Doherty [67] Europe Mostly female Children Chronic Arthritis/ musculoskeletal Outpatient Drayer [68] USA/Canada Both Everett [69] USA/Canada Mostly male Mostly female Acute Burn Outpatient Forrest [70] Europe Acute Fridh [71] USA/Canada Mostly female Adults (18+) Mostly female Acute Pregnancy/labor Inpatient Gil [72] Asia/Middle East Mostly male Adults (18+) Chronic Cancer Inpatient Harrison [73] Asia/Middle East Hays [74] USA/Canada Mostly male Adults (18+) Mostly female Chronic Outpatient Heikkinen [75] Europe Adults (18+) Heuss [76] Europe Mostly male Adults (18+) Acute Cancer Outpatient Europe Mostly male Adults (18+) Acute Cancer Outpatient Higginson [77] Europe Mostly male Older adults only (65+) Mostly female Chronic Cancer Hodgkins [78] USA/Canada Mostly female Adults (18+) Horgas [79] USA/Canada Mostly female Adults (18+) Mostly female Both Outpatient Idvall [80] USA/Canada Mostly female Mostly female Acute Surgery or procedure Kelly [81] Other Children Mostly female Acute Inpatient Kristjanson [82] Other Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Lautenbacher [28] Europe Adults (18+) Mostly female Acute Lab-based procedure Outpatient Europe Adults (18+) Mostly female Acute Lab-based procedure Outpatient Lin [83] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Lobchuk [84] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Lobchuk [20] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Madison [85] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Maguire [86] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Outpatient Manne [87] USA/Canada Mostly male Children Mostly female Chronic Cancer Outpatient USA/Canada Mostly male Children Chronic Cancer Outpatient Mantyselka [5] Europe Mostly female Both Arthritis/musculoskeletal Outpatient McKinley [88] Other Mostly female Adults (18+) Inpatient McMillan [89] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient McPherson [90] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Miller [91] USA/Canada Children Mostly female Acute Surgery or procedure Inpatient USA/Canada Children Acute Surgery or procedure Inpatient O’Brien [92] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Oi-Ling [93] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Paice [94] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient Perreault [95] USA/Canada Mostly female Adults (18+) Mostly female Chronic Arthritis/ musculoskeletal Outpatient Powers [96] USA/Canada Mostly male Children Acute Surgery or procedure Inpatient Prkachin [97] USA/Canada Mostly female Acute Prkachin [98] USA/Canada Acute USA/Canada Mostly male Acute Laboratory Redinbaugh [99] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Resnizky [100] Asia/Middle East Mostly female Older adults only (65+) Mostly female Chronic Inpatient Rhondali [101] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Ruben [7] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Ruben [102] USA/Canada Mostly female Adults (18+) Mostly female Acute Lab-based procedure Laboratory Salmon [103] Europe Mostly male Adults (18+) Mostly female Acute Surgery or procedure Inpatient Schneider [104] USA/Canada Children Mostly female Acute Surgery or procedure Outpatient USA/Canada Children Acute Surgery or procedure Outpatient Shega [105] USA/Canada Mostly female Older adults only (65+) Mostly female Chronic Silveira [106] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Outpatient Singer [107] USA/Canada Mostly male Children Mostly female Acute Outpatient Singer [108] USA/Canada Mostly male Children Mostly male Acute Outpatient USA/Canada Mostly male Acute Outpatient Sjöström [109] Europe Acute Surgery or procedure Inpatient Europe Acute Surgery or procedure Inpatient Sneeuw [110] Other Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Sneeuw [111] Europe Mostly female Adults (18+) Mostly male Chronic Cancer Sneeuw [112] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Europe Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Europe Mostly male Adults (18+) Mostly male Chronic Cancer Inpatient Stephenson [113] USA/Canada Mostly male Adults (18+) Mostly female Acute Surgery or procedure Inpatient St-Laurent-Gagnon [114] USA/Canada Mostly female Children Mostly female Acute Surgery or procedure Outpatient Suarez-Almazor [115] USA/Canada Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient Sutherland [116] USA/Canada Adults (18+) Acute van Herk [117] Europe Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient Europe Mostly female Adults (18+) Chronic Arthritis/ musculoskeletal Outpatient Van Der Does [118] Europe Mostly male Mostly female Acute Burn Vervoort [119] Europe Mostly male Children Mostly female Acute Lab-based procedure Walkenstein [120] USA/Canada Mostly male Acute Burn Weiner [121] USA/Canada Mostly male Adults (18+) Mostly female Chronic Arthritis/musculoskeletal Inpatient USA/Canada Mostly male Adults (18+) Mostly female Chronic Arthritis/musculoskeletal Inpatient Wennman-Larsen [122] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Yesilbalkan [123] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Zalon [124] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient Zhukovsky [125] USA/Canada Mostly male Children Mostly female Chronic Cancer Outpatient USA/Canada Mostly male Children Chronic Cancer Outpatient For country “other” is Australia or multiple countries. View Large Table A2 Moderators present for paired comparison pain assessment meta-analysis First author Country Patient gender Patient age group Observer gender Type of pain Pain condition Location of assessment Akin [56] Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Bergh [57] Europe Older adults only (65+) Chronic Arthritis/musculoskeletal Inpatient Europe Older adults only (65+) Chronic Arthritis/musculoskeletal Inpatient Europe Older adults only (65+) Chronic Arthritis/musculoskeletal Inpatient Bowman [126] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient Broberger [59] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Europe Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Cano [127] USA/Canada Mostly female Adults (18+) Mostly male Chronic Chambers [60] USA/Canada Mostly male Children Mostly female Acute Surgery or procedure Inpatient Chambers [128] USA/Canada Mostly female Children Mostly female Acute Surgery or procedure Outpatient Coran [129] USA/Canada Mostly male Adults (18+) Mostly male Cancer Outpatient Dar [130] USA/Canada Mostly male Mostly female Chronic Cancer Inpatient Dobkin [131] USA/Canada Mostly female Adults (18+) Mostly male Chronic Arthritis/musculoskeletal Everett [69] USA/Canada Mostly male Mostly female Acute Burn Outpatient Forrest [70] Europe Acute Gil [72] Asia/Middle East Mostly female Adults (18+) Chronic Cancer Inpatient Goulet [132] USA/Canada Mostly male Adults (18+) Both Outpatient Green [133] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Guru [134] USA/Canada Mostly female Adults (18+) USA/Canada Mostly female Adults (18+) Hall-Lord [135] Europe Mostly male Older adults only (65+) Mostly female Chronic Inpatient Europe Mostly male Older adults only (65+) Mostly female Chronic Inpatient Hays [74] USA/Canada Mostly male Adults (18+) Mostly female Chronic Outpatient Heikkinen [75] Europe Adults (18+) Heuss [76] Europe Mostly male Adults (18+) Acute Cancer Outpatient Europe Mostly male Adults (18+) Acute Cancer Outpatient Higginson [77] Europe Mostly male Older adults only (65+) Mostly female Chronic Cancer more than one Hodgkins [78] USA/Canada Mostly female Adults (18+) USA/Canada Mostly female Adults (18+) Holmes [136] Europe Chronic Cancer Inpatient Horgas [79] USA/Canada Mostly female Adults (18+) Mostly female Both Outpatient Idvall [137] Europe Mostly female Adults (18+) Mostly female Acute Surgery or procedure Idvall [80] Europe Mostly female Mostly female Acute Surgery or procedure Kappesser [138] Europe Mostly male Mostly male Ketovuori [139] Europe Adults (18+) Acute Surgery or procedure Inpatient Kristjanson [82] Other Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Krivo [140] USA/Canada Chronic USA/Canada Chronic Lamontagne [29] USA/Canada Mostly female Children Acute Surgery or procedure Inpatient USA/Canada Mostly female Children Acute Surgery or procedure Inpatient Laugsand [141] Europe Mostly male Adults (18+) Chronic Cancer Lieberman [142] USA/Canada Mostly female Acute Lin [83] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Lobchuk [84] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Lobchuk [20] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Madison [85] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Maguire [86] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Outpatient Manne [87]a USA/Canada Mostly male Children Chronic Cancer Outpatient Mäntyselkä [5] Europe Mostly female Both Arthritis/musculoskeletal Outpatient Marquié [143] Europe Mostly male Mostly male Acute Martire [144] USA/Canada Mostly female Older adults only (65+) Chronic Arthritis/musculoskeletal Outpatient McMillan [89] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient McPherson [90] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Melotti [145] Europe Mostly female Both more than one Milne [146] Other Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Modić Stanke [147] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Europe Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Molassiotis [148] Europe Mostly female Adults (18+) Chronic Cancer Inpatient Oechsle [26] Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Outpatient USA/Canada Mostly female Adults (18+) Mostly female Chronic Arthritis/musculoskeletal Outpatient Ovayolu [149] USA/Canada Mostly female Adults (18+) Mostly female Acute Arthritis/musculoskeletal Perreault [150] USA/Canada Mostly female Acute Perreault [95] USA/Canada Adults (18+) Acute Outpatient Prkachin [97] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Pronina [151] Europe Mostly female Adults (18+) Mostly male Chronic Arthritis/musculoskeletal Puntillo [152] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Redinbaugh [99] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Riemsma [153] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Robinson [154] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Ruben [7] USA/Canada Mostly female Adults (18+) Mostly female Acute Lab-based procedure Laboratory USA/Canada Mostly male Adults (18+) Mostly male Acute Lab-based procedure Laboratory Europe Mostly female Older adults only (65+) Chronic Outpatient Ruben [102] Europe Mostly female Older adults only (65+) Chronic Outpatient Santos [155] USA/Canada Children Mostly female Acute Surgery or procedure Outpatient USA/Canada Children Acute Surgery or procedure Outpatient Schneider [104] USA/Canada Mostly male Adults (18+) Mostly female Both Outpatient USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Outpatient Shugarman [156] Europe Acute Surgery or procedure Inpatient Silveria [106] Asia/Middle East Mostly male Adults (18+) Mostly female Acute Surgery or procedure Inpatient Sjöström [109] Other Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Sloman [157] Europe Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Sneeuw [110] Europe Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Sneeuw [111] Europe Mostly female Adults (18+) Mostly male Chronic Cancer Inpatient Sneeuw [112] Europe Mostly female Adults (18+) Mostly male Chronic Cancer more than one Asia/Middle East Mostly male Adults (18+) Acute Asia/Middle East Mostly male Adults (18+) Acute Stalnikowicz [158] Europe Mostly female Adults (18+) Mostly female Acute Lab-based procedure Laboratory Europe Mostly female Adults (18+) Mostly female Acute Lab-based procedure Laboratory Stephenson [113] USA/Canada Mostly female Adults (18+) Mostly female Acute Surgery or procedure Inpatient Suarez-Almazor [115] USA/Canada Mostly female Adults (18+) Chronic Arthritis/musculoskeletal Outpatient Sullivan [159] USA/Canada Mostly female Mostly female Acute Lab-based procedure Laboratory Sullivan [160] USA/Canada Mostly male Mostly male Acute Lab-based procedure Laboratory Sutherland [116] USA/Canada Adults (18+) Acute Thomas [161] USA/Canada Mostly male Adults (18+) Acute Todd [162] USA/Canada Acute Outpatient Van der Does [118] Europe Mostly male Mostly female Acute Burn Vervoort [119] Europe Mostly male Children Mostly female Acute Lab-based procedure Walkenstein [120] USA/Canada Mostly male Acute Burn Wennman-Larsen [122] Europe Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Yeager [163] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Outpatient Yesilbalkan [123] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Zalon [124] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient First author Country Patient gender Patient age group Observer gender Type of pain Pain condition Location of assessment Akin [56] Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Bergh [57] Europe Older adults only (65+) Chronic Arthritis/musculoskeletal Inpatient Europe Older adults only (65+) Chronic Arthritis/musculoskeletal Inpatient Europe Older adults only (65+) Chronic Arthritis/musculoskeletal Inpatient Bowman [126] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient Broberger [59] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Europe Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Cano [127] USA/Canada Mostly female Adults (18+) Mostly male Chronic Chambers [60] USA/Canada Mostly male Children Mostly female Acute Surgery or procedure Inpatient Chambers [128] USA/Canada Mostly female Children Mostly female Acute Surgery or procedure Outpatient Coran [129] USA/Canada Mostly male Adults (18+) Mostly male Cancer Outpatient Dar [130] USA/Canada Mostly male Mostly female Chronic Cancer Inpatient Dobkin [131] USA/Canada Mostly female Adults (18+) Mostly male Chronic Arthritis/musculoskeletal Everett [69] USA/Canada Mostly male Mostly female Acute Burn Outpatient Forrest [70] Europe Acute Gil [72] Asia/Middle East Mostly female Adults (18+) Chronic Cancer Inpatient Goulet [132] USA/Canada Mostly male Adults (18+) Both Outpatient Green [133] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Guru [134] USA/Canada Mostly female Adults (18+) USA/Canada Mostly female Adults (18+) Hall-Lord [135] Europe Mostly male Older adults only (65+) Mostly female Chronic Inpatient Europe Mostly male Older adults only (65+) Mostly female Chronic Inpatient Hays [74] USA/Canada Mostly male Adults (18+) Mostly female Chronic Outpatient Heikkinen [75] Europe Adults (18+) Heuss [76] Europe Mostly male Adults (18+) Acute Cancer Outpatient Europe Mostly male Adults (18+) Acute Cancer Outpatient Higginson [77] Europe Mostly male Older adults only (65+) Mostly female Chronic Cancer more than one Hodgkins [78] USA/Canada Mostly female Adults (18+) USA/Canada Mostly female Adults (18+) Holmes [136] Europe Chronic Cancer Inpatient Horgas [79] USA/Canada Mostly female Adults (18+) Mostly female Both Outpatient Idvall [137] Europe Mostly female Adults (18+) Mostly female Acute Surgery or procedure Idvall [80] Europe Mostly female Mostly female Acute Surgery or procedure Kappesser [138] Europe Mostly male Mostly male Ketovuori [139] Europe Adults (18+) Acute Surgery or procedure Inpatient Kristjanson [82] Other Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Krivo [140] USA/Canada Chronic USA/Canada Chronic Lamontagne [29] USA/Canada Mostly female Children Acute Surgery or procedure Inpatient USA/Canada Mostly female Children Acute Surgery or procedure Inpatient Laugsand [141] Europe Mostly male Adults (18+) Chronic Cancer Lieberman [142] USA/Canada Mostly female Acute Lin [83] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Lobchuk [84] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Lobchuk [20] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Madison [85] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Maguire [86] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Outpatient Manne [87]a USA/Canada Mostly male Children Chronic Cancer Outpatient Mäntyselkä [5] Europe Mostly female Both Arthritis/musculoskeletal Outpatient Marquié [143] Europe Mostly male Mostly male Acute Martire [144] USA/Canada Mostly female Older adults only (65+) Chronic Arthritis/musculoskeletal Outpatient McMillan [89] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient McPherson [90] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Melotti [145] Europe Mostly female Both more than one Milne [146] Other Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Modić Stanke [147] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Europe Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Molassiotis [148] Europe Mostly female Adults (18+) Chronic Cancer Inpatient Oechsle [26] Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Outpatient USA/Canada Mostly female Adults (18+) Mostly female Chronic Arthritis/musculoskeletal Outpatient Ovayolu [149] USA/Canada Mostly female Adults (18+) Mostly female Acute Arthritis/musculoskeletal Perreault [150] USA/Canada Mostly female Acute Perreault [95] USA/Canada Adults (18+) Acute Outpatient Prkachin [97] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Pronina [151] Europe Mostly female Adults (18+) Mostly male Chronic Arthritis/musculoskeletal Puntillo [152] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Redinbaugh [99] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Riemsma [153] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Robinson [154] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Ruben [7] USA/Canada Mostly female Adults (18+) Mostly female Acute Lab-based procedure Laboratory USA/Canada Mostly male Adults (18+) Mostly male Acute Lab-based procedure Laboratory Europe Mostly female Older adults only (65+) Chronic Outpatient Ruben [102] Europe Mostly female Older adults only (65+) Chronic Outpatient Santos [155] USA/Canada Children Mostly female Acute Surgery or procedure Outpatient USA/Canada Children Acute Surgery or procedure Outpatient Schneider [104] USA/Canada Mostly male Adults (18+) Mostly female Both Outpatient USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Outpatient Shugarman [156] Europe Acute Surgery or procedure Inpatient Silveria [106] Asia/Middle East Mostly male Adults (18+) Mostly female Acute Surgery or procedure Inpatient Sjöström [109] Other Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Sloman [157] Europe Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Sneeuw [110] Europe Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Sneeuw [111] Europe Mostly female Adults (18+) Mostly male Chronic Cancer Inpatient Sneeuw [112] Europe Mostly female Adults (18+) Mostly male Chronic Cancer more than one Asia/Middle East Mostly male Adults (18+) Acute Asia/Middle East Mostly male Adults (18+) Acute Stalnikowicz [158] Europe Mostly female Adults (18+) Mostly female Acute Lab-based procedure Laboratory Europe Mostly female Adults (18+) Mostly female Acute Lab-based procedure Laboratory Stephenson [113] USA/Canada Mostly female Adults (18+) Mostly female Acute Surgery or procedure Inpatient Suarez-Almazor [115] USA/Canada Mostly female Adults (18+) Chronic Arthritis/musculoskeletal Outpatient Sullivan [159] USA/Canada Mostly female Mostly female Acute Lab-based procedure Laboratory Sullivan [160] USA/Canada Mostly male Mostly male Acute Lab-based procedure Laboratory Sutherland [116] USA/Canada Adults (18+) Acute Thomas [161] USA/Canada Mostly male Adults (18+) Acute Todd [162] USA/Canada Acute Outpatient Van der Does [118] Europe Mostly male Mostly female Acute Burn Vervoort [119] Europe Mostly male Children Mostly female Acute Lab-based procedure Walkenstein [120] USA/Canada Mostly male Acute Burn Wennman-Larsen [122] Europe Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Yeager [163] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Outpatient Yesilbalkan [123] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Zalon [124] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient View Large Table A2 Moderators present for paired comparison pain assessment meta-analysis First author Country Patient gender Patient age group Observer gender Type of pain Pain condition Location of assessment Akin [56] Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Bergh [57] Europe Older adults only (65+) Chronic Arthritis/musculoskeletal Inpatient Europe Older adults only (65+) Chronic Arthritis/musculoskeletal Inpatient Europe Older adults only (65+) Chronic Arthritis/musculoskeletal Inpatient Bowman [126] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient Broberger [59] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Europe Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Cano [127] USA/Canada Mostly female Adults (18+) Mostly male Chronic Chambers [60] USA/Canada Mostly male Children Mostly female Acute Surgery or procedure Inpatient Chambers [128] USA/Canada Mostly female Children Mostly female Acute Surgery or procedure Outpatient Coran [129] USA/Canada Mostly male Adults (18+) Mostly male Cancer Outpatient Dar [130] USA/Canada Mostly male Mostly female Chronic Cancer Inpatient Dobkin [131] USA/Canada Mostly female Adults (18+) Mostly male Chronic Arthritis/musculoskeletal Everett [69] USA/Canada Mostly male Mostly female Acute Burn Outpatient Forrest [70] Europe Acute Gil [72] Asia/Middle East Mostly female Adults (18+) Chronic Cancer Inpatient Goulet [132] USA/Canada Mostly male Adults (18+) Both Outpatient Green [133] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Guru [134] USA/Canada Mostly female Adults (18+) USA/Canada Mostly female Adults (18+) Hall-Lord [135] Europe Mostly male Older adults only (65+) Mostly female Chronic Inpatient Europe Mostly male Older adults only (65+) Mostly female Chronic Inpatient Hays [74] USA/Canada Mostly male Adults (18+) Mostly female Chronic Outpatient Heikkinen [75] Europe Adults (18+) Heuss [76] Europe Mostly male Adults (18+) Acute Cancer Outpatient Europe Mostly male Adults (18+) Acute Cancer Outpatient Higginson [77] Europe Mostly male Older adults only (65+) Mostly female Chronic Cancer more than one Hodgkins [78] USA/Canada Mostly female Adults (18+) USA/Canada Mostly female Adults (18+) Holmes [136] Europe Chronic Cancer Inpatient Horgas [79] USA/Canada Mostly female Adults (18+) Mostly female Both Outpatient Idvall [137] Europe Mostly female Adults (18+) Mostly female Acute Surgery or procedure Idvall [80] Europe Mostly female Mostly female Acute Surgery or procedure Kappesser [138] Europe Mostly male Mostly male Ketovuori [139] Europe Adults (18+) Acute Surgery or procedure Inpatient Kristjanson [82] Other Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Krivo [140] USA/Canada Chronic USA/Canada Chronic Lamontagne [29] USA/Canada Mostly female Children Acute Surgery or procedure Inpatient USA/Canada Mostly female Children Acute Surgery or procedure Inpatient Laugsand [141] Europe Mostly male Adults (18+) Chronic Cancer Lieberman [142] USA/Canada Mostly female Acute Lin [83] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Lobchuk [84] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Lobchuk [20] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Madison [85] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Maguire [86] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Outpatient Manne [87]a USA/Canada Mostly male Children Chronic Cancer Outpatient Mäntyselkä [5] Europe Mostly female Both Arthritis/musculoskeletal Outpatient Marquié [143] Europe Mostly male Mostly male Acute Martire [144] USA/Canada Mostly female Older adults only (65+) Chronic Arthritis/musculoskeletal Outpatient McMillan [89] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient McPherson [90] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Melotti [145] Europe Mostly female Both more than one Milne [146] Other Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Modić Stanke [147] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Europe Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Molassiotis [148] Europe Mostly female Adults (18+) Chronic Cancer Inpatient Oechsle [26] Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Outpatient USA/Canada Mostly female Adults (18+) Mostly female Chronic Arthritis/musculoskeletal Outpatient Ovayolu [149] USA/Canada Mostly female Adults (18+) Mostly female Acute Arthritis/musculoskeletal Perreault [150] USA/Canada Mostly female Acute Perreault [95] USA/Canada Adults (18+) Acute Outpatient Prkachin [97] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Pronina [151] Europe Mostly female Adults (18+) Mostly male Chronic Arthritis/musculoskeletal Puntillo [152] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Redinbaugh [99] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Riemsma [153] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Robinson [154] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Ruben [7] USA/Canada Mostly female Adults (18+) Mostly female Acute Lab-based procedure Laboratory USA/Canada Mostly male Adults (18+) Mostly male Acute Lab-based procedure Laboratory Europe Mostly female Older adults only (65+) Chronic Outpatient Ruben [102] Europe Mostly female Older adults only (65+) Chronic Outpatient Santos [155] USA/Canada Children Mostly female Acute Surgery or procedure Outpatient USA/Canada Children Acute Surgery or procedure Outpatient Schneider [104] USA/Canada Mostly male Adults (18+) Mostly female Both Outpatient USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Outpatient Shugarman [156] Europe Acute Surgery or procedure Inpatient Silveria [106] Asia/Middle East Mostly male Adults (18+) Mostly female Acute Surgery or procedure Inpatient Sjöström [109] Other Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Sloman [157] Europe Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Sneeuw [110] Europe Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Sneeuw [111] Europe Mostly female Adults (18+) Mostly male Chronic Cancer Inpatient Sneeuw [112] Europe Mostly female Adults (18+) Mostly male Chronic Cancer more than one Asia/Middle East Mostly male Adults (18+) Acute Asia/Middle East Mostly male Adults (18+) Acute Stalnikowicz [158] Europe Mostly female Adults (18+) Mostly female Acute Lab-based procedure Laboratory Europe Mostly female Adults (18+) Mostly female Acute Lab-based procedure Laboratory Stephenson [113] USA/Canada Mostly female Adults (18+) Mostly female Acute Surgery or procedure Inpatient Suarez-Almazor [115] USA/Canada Mostly female Adults (18+) Chronic Arthritis/musculoskeletal Outpatient Sullivan [159] USA/Canada Mostly female Mostly female Acute Lab-based procedure Laboratory Sullivan [160] USA/Canada Mostly male Mostly male Acute Lab-based procedure Laboratory Sutherland [116] USA/Canada Adults (18+) Acute Thomas [161] USA/Canada Mostly male Adults (18+) Acute Todd [162] USA/Canada Acute Outpatient Van der Does [118] Europe Mostly male Mostly female Acute Burn Vervoort [119] Europe Mostly male Children Mostly female Acute Lab-based procedure Walkenstein [120] USA/Canada Mostly male Acute Burn Wennman-Larsen [122] Europe Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Yeager [163] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Outpatient Yesilbalkan [123] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Zalon [124] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient First author Country Patient gender Patient age group Observer gender Type of pain Pain condition Location of assessment Akin [56] Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Bergh [57] Europe Older adults only (65+) Chronic Arthritis/musculoskeletal Inpatient Europe Older adults only (65+) Chronic Arthritis/musculoskeletal Inpatient Europe Older adults only (65+) Chronic Arthritis/musculoskeletal Inpatient Bowman [126] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient Broberger [59] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Europe Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Cano [127] USA/Canada Mostly female Adults (18+) Mostly male Chronic Chambers [60] USA/Canada Mostly male Children Mostly female Acute Surgery or procedure Inpatient Chambers [128] USA/Canada Mostly female Children Mostly female Acute Surgery or procedure Outpatient Coran [129] USA/Canada Mostly male Adults (18+) Mostly male Cancer Outpatient Dar [130] USA/Canada Mostly male Mostly female Chronic Cancer Inpatient Dobkin [131] USA/Canada Mostly female Adults (18+) Mostly male Chronic Arthritis/musculoskeletal Everett [69] USA/Canada Mostly male Mostly female Acute Burn Outpatient Forrest [70] Europe Acute Gil [72] Asia/Middle East Mostly female Adults (18+) Chronic Cancer Inpatient Goulet [132] USA/Canada Mostly male Adults (18+) Both Outpatient Green [133] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Guru [134] USA/Canada Mostly female Adults (18+) USA/Canada Mostly female Adults (18+) Hall-Lord [135] Europe Mostly male Older adults only (65+) Mostly female Chronic Inpatient Europe Mostly male Older adults only (65+) Mostly female Chronic Inpatient Hays [74] USA/Canada Mostly male Adults (18+) Mostly female Chronic Outpatient Heikkinen [75] Europe Adults (18+) Heuss [76] Europe Mostly male Adults (18+) Acute Cancer Outpatient Europe Mostly male Adults (18+) Acute Cancer Outpatient Higginson [77] Europe Mostly male Older adults only (65+) Mostly female Chronic Cancer more than one Hodgkins [78] USA/Canada Mostly female Adults (18+) USA/Canada Mostly female Adults (18+) Holmes [136] Europe Chronic Cancer Inpatient Horgas [79] USA/Canada Mostly female Adults (18+) Mostly female Both Outpatient Idvall [137] Europe Mostly female Adults (18+) Mostly female Acute Surgery or procedure Idvall [80] Europe Mostly female Mostly female Acute Surgery or procedure Kappesser [138] Europe Mostly male Mostly male Ketovuori [139] Europe Adults (18+) Acute Surgery or procedure Inpatient Kristjanson [82] Other Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Krivo [140] USA/Canada Chronic USA/Canada Chronic Lamontagne [29] USA/Canada Mostly female Children Acute Surgery or procedure Inpatient USA/Canada Mostly female Children Acute Surgery or procedure Inpatient Laugsand [141] Europe Mostly male Adults (18+) Chronic Cancer Lieberman [142] USA/Canada Mostly female Acute Lin [83] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Lobchuk [84] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Lobchuk [20] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Madison [85] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Maguire [86] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Outpatient Manne [87]a USA/Canada Mostly male Children Chronic Cancer Outpatient Mäntyselkä [5] Europe Mostly female Both Arthritis/musculoskeletal Outpatient Marquié [143] Europe Mostly male Mostly male Acute Martire [144] USA/Canada Mostly female Older adults only (65+) Chronic Arthritis/musculoskeletal Outpatient McMillan [89] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient McPherson [90] USA/Canada Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Melotti [145] Europe Mostly female Both more than one Milne [146] Other Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Modić Stanke [147] Europe Mostly male Adults (18+) Mostly female Chronic Cancer Inpatient Europe Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Molassiotis [148] Europe Mostly female Adults (18+) Chronic Cancer Inpatient Oechsle [26] Asia/Middle East Mostly female Adults (18+) Mostly female Chronic Cancer Outpatient USA/Canada Mostly female Adults (18+) Mostly female Chronic Arthritis/musculoskeletal Outpatient Ovayolu [149] USA/Canada Mostly female Adults (18+) Mostly female Acute Arthritis/musculoskeletal Perreault [150] USA/Canada Mostly female Acute Perreault [95] USA/Canada Adults (18+) Acute Outpatient Prkachin [97] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Pronina [151] Europe Mostly female Adults (18+) Mostly male Chronic Arthritis/musculoskeletal Puntillo [152] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Redinbaugh [99] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Riemsma [153] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Robinson [154] USA/Canada Mostly male Mostly female Acute Lab-based procedure Laboratory Ruben [7] USA/Canada Mostly female Adults (18+) Mostly female Acute Lab-based procedure Laboratory USA/Canada Mostly male Adults (18+) Mostly male Acute Lab-based procedure Laboratory Europe Mostly female Older adults only (65+) Chronic Outpatient Ruben [102] Europe Mostly female Older adults only (65+) Chronic Outpatient Santos [155] USA/Canada Children Mostly female Acute Surgery or procedure Outpatient USA/Canada Children Acute Surgery or procedure Outpatient Schneider [104] USA/Canada Mostly male Adults (18+) Mostly female Both Outpatient USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Outpatient Shugarman [156] Europe Acute Surgery or procedure Inpatient Silveria [106] Asia/Middle East Mostly male Adults (18+) Mostly female Acute Surgery or procedure Inpatient Sjöström [109] Other Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Sloman [157] Europe Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Sneeuw [110] Europe Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Sneeuw [111] Europe Mostly female Adults (18+) Mostly male Chronic Cancer Inpatient Sneeuw [112] Europe Mostly female Adults (18+) Mostly male Chronic Cancer more than one Asia/Middle East Mostly male Adults (18+) Acute Asia/Middle East Mostly male Adults (18+) Acute Stalnikowicz [158] Europe Mostly female Adults (18+) Mostly female Acute Lab-based procedure Laboratory Europe Mostly female Adults (18+) Mostly female Acute Lab-based procedure Laboratory Stephenson [113] USA/Canada Mostly female Adults (18+) Mostly female Acute Surgery or procedure Inpatient Suarez-Almazor [115] USA/Canada Mostly female Adults (18+) Chronic Arthritis/musculoskeletal Outpatient Sullivan [159] USA/Canada Mostly female Mostly female Acute Lab-based procedure Laboratory Sullivan [160] USA/Canada Mostly male Mostly male Acute Lab-based procedure Laboratory Sutherland [116] USA/Canada Adults (18+) Acute Thomas [161] USA/Canada Mostly male Adults (18+) Acute Todd [162] USA/Canada Acute Outpatient Van der Does [118] Europe Mostly male Mostly female Acute Burn Vervoort [119] Europe Mostly male Children Mostly female Acute Lab-based procedure Walkenstein [120] USA/Canada Mostly male Acute Burn Wennman-Larsen [122] Europe Mostly female Adults (18+) Mostly female Chronic Cancer Inpatient Yeager [163] USA/Canada Mostly female Adults (18+) Mostly female Chronic Cancer Outpatient Yesilbalkan [123] Asia/Middle East Mostly male Adults (18+) Mostly female Chronic Cancer Outpatient Zalon [124] USA/Canada Mostly female Adults (18+) Acute Surgery or procedure Inpatient View 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To Know Another’s Pain: A Meta-analysis of Caregivers’ and Healthcare Providers’ Pain Assessment Accuracy