The British Journal of Social Work

Kotera,, Y;Green,, P;Sheffield,, D

doi: 10.1093/bjsw/bcz149pmid: N/A

Abstract Despite high shame about mental health symptoms among UK social work students, positive psychological approaches to their mental health have not been investigated in depth. Emotional resilience has been a core skill in social work practice; however, its relationship with mental health is still unclear. Therefore, the primary purposes of this cross-sectional study were to (i) examine the relationships between mental health and positive psychological constructs, namely resilience, self-compassion, motivation and engagement and (ii) determine predictors of mental health in UK social work students. An opportunity sampling of 116 UK social work students (102 females, 14 males; 96 undergraduates, 20 postgraduates) completed 5 measures about these constructs. Correlation and regression analyses were conducted. Mental health was associated with resilience, self-compassion and engagement. Self-compassion was a negative predictor, and intrinsic motivation was a positive predictor of mental health symptoms. Resilience did not predict mental health symptoms. The findings highlight the importance of self-compassion to the challenging mental health of UK social work students; they caution against the overuse and misunderstanding of resilience in the social work field. mental health, positive psychology, resilience, self-compassion, social work education Introduction Mental health of social work students While social work is a popular degree subject receiving more than 12,000 applications annually (Holmström, 2010), social work students suffer from high rates of mental health problems. Approximately, 25 per cent of UK university students suffer from some level of mental health problems (Aronin and Smith, 2016). More social work students have high levels of depressive symptoms (34 per cent), indicating high risk of clinical depression, 40 per cent reported having suicidal thoughts in their lives and 4 per cent reported recent suicidal thoughts (Horton et al., 2009). Unsurprisingly, poor mental health is related to limited academic achievement and higher dropout (Poh-Keong et al., 2015). Poor mental health is problematic as social work graduates enter an emotionally challenging profession with the highest rate of work-related stress in the UK (Health and Safety Executive, 2017). The majority of students progress towards employment in the social work field (Association of Graduate Careers Advisory Services, 2015). In the UK, 70 per cent of graduates were employed as a social worker within 6 months of graduation, creating nearly 2,900 qualified social workers in employment in 2015, and the number has been increasing (Skills for Care, 2016). However, 80 per cent of social workers feel emotional distress and 40 per cent are verbally abused at work (Community Care and UNISON, 2016). Considering that much of the political attention recently has been placed on Brexit, duties of social workers are likely to be heavier ensuring that the nation’s mental health initiatives are not overlooked (Golightley and Holloway, 2019). Social workers negotiate a wide range of conflicting roles and duties in their daily practice, which can be mentally challenging—suggesting a need to review their education (Cartney, 2018). Mentally distressed workers struggle to utilise their creativity (Dunnagan et al., 2001), and tend to limit their work activities (Gilmour and Patten, 2007). For example, depression compromises productivity, and can lead to disability, absenteeism and premature early retirement (Blackmore et al., 2007). Under-performance in social work can cause detrimental consequences in both practitioners and service users, as risk management is essential in social work (Hardy, 2017). These findings highlight the seriousness of poor mental health in social work students and practitioners. However, health of social workers and students in the UK has been under-recognised (McCusker and Jackson, 2016). Canadian social work students, who undertook text-based online counselling, reported that experiences of improving their mental health gave them confidence that they would be better able to cope with future mental distress (Fang et al., 2018): improvement of mental health can have positive long-lasting effects for social work students, suggesting great value of exploring their mental health. Due to the advancement of technologies (e.g. the fourth industrial revolution) and the commercialisation of education (e.g. mega-universities), agile anticipative education curricula are being sought after (Neden et al., 2019): the mental health and self-care of social work students should be a focus of today’s social work education. Resilience in social work practice While the definition of emotional resilience (hereafter ‘resilience’) has been diverse (e.g. Ungar, 2006; Pooley and Cohen, 2010), resilience in social work can be defined as the ability to practice professionally utilising empathy, optimism, stability, honesty and self-awareness (Green, 2016). It can be considered as an umbrella term encompassing internal resources and behaviours, helping people cope with adversity and develop themselves from such experiences (Grant and Kinman, 2014). Originating from Werner’s Hawaiian study in 1951, which found that many children in high-risk environments grew up to be caring and competent adults (Werner et al., 1971), resilience has increasingly been a focus in social work (Collins, 2017). Resilience was identified as a core skill (Crampton, 2015), highlighted in the Professional Capabilities Framework (British Association of Social Workers [BASW], 2018a), and essential for successful social work practice (Social Work Task Force, 2009). Resilience helps individuals focus on positives including strengths and agency, instead of negatives such as weaknesses and vulnerability, reframing views that adversity can be a growth opportunity (Harrison, 2013). Resilience also helps social workers cope with difficult situations such as bereavement and post-traumatic stress (Bonanno, 2004), as well as levels of daily life stress (Collins, 2007). This does not mean that resilient people are not affected by negative events: they may be affected by those events, but they are not overwhelmed by them chronically (Tugade and Fredrickson, 2004). Resilient people learn new knowledge and skills to cope with present and future incidents (Carver, 1998). A recent review (Robertson et al., 2015) reported that enhanced resilience was related to better mental health, strengthening other psychological outcomes including self-efficacy, mindfulness and compassion. Moreover, Bryan et al.’s review revealed that resilience was developed and maintained with psychological resources such as motivation, self-regulation, optimism and a positive mindset. These resources, relating to coping with stress and reframing adversities to learning and development (Bryan et al., 2017), are especially important to emotionally challenging professions such as social work; therefore, it would be valuable to examine relationships between mental health and resilience. Unsurprisingly, studies have reported important effects of resilience in social work. A UK study of 240 social work students revealed that resilience was negatively related to psychological distress, and that emotional and social skills (key skills in social work) predicted 47 per cent of the variance in resilience (Kinman and Grant, 2011). An American study of 314 social work students reported that resilience was negatively related to academic stress, and positively related to social support (Wilks, 2008). Furthermore, resilience and coping skills were significantly related to each other among seventy-three female social work students in India (Stanley and Bhuvaneswari, 2016). These findings suggest that resilience is crucial in social work: however, its relationship with positive psychological constructs has not been explored in-depth. Self-compassion, motivation and engagement UK social work students are often hesitant to ask for help for mental health problems because of shame and negative attitudes towards them (Kotera et al., 2019b). Instead of approaching mental health problems directly, strengthening positive psychological constructs may be an effective helping approach. Positive psychology emphasises happiness, well-being and positivity (Seligman and Csikszentmihalyi, 2000). Although moderately interrelated to mental health (Weich et al., 2011), some positive psychology constructs have been the focus of researchers and practitioners. In particular, self-compassion, an understanding and kindness to the self during difficult times (Gilbert, 2010), has been found to contribute to mental health by augmenting resilience (Trompetter et al., 2017). Self-compassion is related to good mental health, and higher levels of self-compassion may reduce mental health problems (Muris et al., 2016). Self-compassion is positively related to resilience and negatively related to mental health problems (Hayter and Dorstyn, 2014). Intrinsic motivation, a key component of self-determination theory (SDT), is also a determinant of mental health (Baard et al., 2004; Locke and Latham, 2004; Bailey and Phillips, 2016). SDT is an established motivation theory presuming that human beings have a natural proclivity to concentrate their psychological energy into a sense of self and larger social structures (Deci and Ryan, 1985). SDT discerns intrinsic from extrinsic forms of motivation: intrinsic motivation relates to activities undertaken as they are inherently interesting and satisfying, while extrinsic motivation relates to activities undertaken for instrumental reasons such as money or status. Intrinsic motivation is associated with better goal achievement (Sheldon and Elliot, 1998), performance (Baard et al., 2004), well-being (Bailey and Phillips, 2016), job and life satisfaction (Locke and Latham, 2004) and prosocial behaviour (Gagne, 2003). Conversely, extrinsic motivation is related to negative outcomes (Vallerand and Ratelle, 2002) such as emotional exhaustion (Houkes et al., 2003), depression (Blais et al., 1993) and reduced performance (Vallerand, 1997). Although caring profession students’ intrinsic motivation was related to academic performance (Khalaila, 2015) and meaningfulness (Utvær, 2014), no study till date has explored the relationship between these types of motivation, mental health and resilience in UK social work students. Finally, academic engagement is also related to mental health in students (Rogers et al., 2017). Academic engagement can be defined as learners’ efforts towards academic goals, including their determination in acquiring knowledge and mastering tasks (Newman et al., 1992). Academic engagement is related to numerous positive outcomes, including achievement (Casuso-Holgado et al., 2013), autonomous learning (Armbruster et al., 2009) and mental health (Rogers et al., 2017). For example, an Australian study of 410 students reported that resilience was related to their academic engagement and mental health: more resilient students were more engaged in their academic activities, and had less mental health problems (Turner et al., 2017). Despite the strong relationships between resilience, self-compassion, motivation, engagement and mental health, to date, these relationships have not been determined in social work students. Aim and hypotheses Based on these findings, therefore, this study aimed to explore the relationships between mental health, resilience, self-compassion, motivation and engagement in UK social work students. We hypothesised that: H1: Resilience, self-compassion, motivation and engagement would be related to mental health symptoms; and H2: Those same variables would serve as negative independent predictors of mental health symptoms. Methods Participants Participants, aged eighteen years or older, were social work students at a UK university in the Midlands of England. Of 120 full-time undergraduate and postgraduate students who were asked to participate in this study, 116 (102 females, 14 males; 96 undergraduates, 20 postgraduates; Mage = 30.88, SDage = 9.39, Age Range; RNGage = 18–58 years) completed the 5 measures: resilience, self-compassion, motivation, engagement and mental health. One hundred and two students were from the UK, three were from other European countries, nine were African and two did not answer. Instruments Depression Anxiety and Stress Scale (DASS21) is a short form of the DASS42 (Lovibond and Lovibond, 1995) measuring mental health. This 21-item self-report scale comprises three subscales to measure levels of depression (e.g. ‘I couldn’t seem to experience any positive feeling at all’), anxiety (e.g. ‘I was aware of dryness of my mouth’) and stress (e.g. ‘I found it hard to wind down’). Students score how much each statement applied to them over the past week, on a four-point Likert scale (0 = ‘Did not apply to me at all’ to 3 = ‘Applied to me very much, or most of the time’). In this study, the total score was used to capture the levels of students’ mental health as the subscales were strongly related to each other (r = 0.62–0.78). The total score of DASS21 has high internal consistency (α = 0.93) (Henry and Crawford, 2005). Brief Resilience Scale (BRS) is a six-item measure, evaluating the ability to bounce back from difficulties (Smith et al., 2008). The six items include ‘I have a hard time making it through stressful events’ on five-point Likert scale (1 = ‘Strongly Disagree’ to 5 = ‘Strongly Agree’) for the items 1, 3 and 5, and the rest of the items are responded reversely (5 = ‘Strongly Disagree’ to 1 = ‘Strongly Agree’). BRS has high internal consistency (α = 0.80–0.91; Smith et al., 2008). Self-Compassion Scale-Short Form (SCS-SF) is a shortened version of the SCS, comprising twelve five-point Likert items (Raes et al., 2011) including ‘I try to be understanding and patient towards those aspects of my personality I don’t like’. The five-point response indicates ‘1’ being ‘Almost never’ to ‘5’ being ‘Almost always’. SCS-SF’s internal consistency was high (α = 0.86; Raes et al., 2011). The Academic Motivation Scale, a 28-item measure, evaluates the levels of seven different types of motivation: amotivation, three types of extrinsic motivation (external, introjected and identified regulation) and three types of intrinsic motivation (to know, to accomplish and to experience stimulation). Amotivation refers to no motivation; an amotivated student is not interested in his/her academic work at all (Deci and Ryan, 1985). Extrinsic motivation relates to behaviours employed, as a means to an end, thus not for their own fulfilment; an extrinsically motivated student may study because his/her parents tell them to do so (Deci and Ryan, 1985). Intrinsic motivation pertains to inherently satisfying behaviours: an intrinsically motivated student may read books for the sheer pleasure of learning something new (Deci and Ryan, 1985). Each type of motivation is evaluated using four items on a seven-point Likert scale (1 = ‘Does not correspond at all’ to 7 = ‘Corresponds exactly’). All subscales have adequate Cronbach’s α (= 0.62–0.91) (Vallerand et al., 1992). For the purposes of this study, the three levels of extrinsic motivation were combined and so were intrinsic motivation. Utrecht Work Engagement Scale for Students (UWES-S) consists of seventeen items, considering to what degree students feel active and adequate towards their academic activities (Schaufeli and Bakker, 2004). There are three subscales in UWES-S: vigour (six items; e.g. ‘When I'm doing my work as a student, I feel bursting with energy’), dedication (five items; e.g. ‘I am enthusiastic about my studies’) and absorption (six items; e.g. ‘When I am studying, I forget everything else around me’). Vigour relates to high levels of energy and the willingness to make an effort in one’s academic work persistently; dedication refers to high involvement in one’s academic work; and absorption is described as full concentration and positive engrossment in one’s academic work (Schaufeli et al., 2002). Items are rated on a seven-point Likert scale, from ‘0’ being ‘Never’ to ‘6’ being ‘Always (everyday)’. All of the subscales demonstrated high internal consistency (α = 0.63–0.81) (Schaufeli and Bakker, 2004). For the purposes of this study, the average of the total score for the engagement measure was used (α = 0.91; Schaufeli and Bakker, 2004). The University Research Ethics Committee granted the ethics of this study. Students were informed about arbitrary participation and withdrawal prior to consent. Should, Information about available mental health support inside and outside the university was provided in case students were distressed from participating in this study. Data analysis A cross-sectional design was employed, addressing the needs for quantitative research in social work (Scourfield et al., 2018). Data were screened for parametric assumptions and descriptive statistics were calculated. Pearson’s correlations were calculated to explore relationships among mental health, resilience, self-compassion, motivation and engagement (H1), followed by multiple regression analyses to identify independent predictors of mental health (H2), using IBM SPSS version 24.0. Results Descriptive statistics One score in stress and extrinsic motivation were identified as outliers using the outlier labelling rule (Hoaglin and Iglewicz, 1987) and so were winsorised (Tukey, 1962). Internal consistencies for all the sub/scales of our sample were high (α ≥ 0.80). Relationships among positive psychological constructs and mental health Scores in six subscales were not normally distributed (Shapiro-Wilk’s test p < 0.05), so all variables were square-root-transformed. For gender, point-biserial coefficients were reported (1 = female, 0 = male; Field, 2018). Mental health symptoms were negatively related to age, resilience, self-compassion and engagement; however, they were not related to any type of motivation (Table 1); H1 was only partially supported. Table 1 Descriptive statistics and correlations among demographics, mental health symptoms, resilience, self-compassion, motivation, engagement in 116 UK social work students Variables M SD α 1 2 3 4 5 6 7 8 9 1. Gender(1 = female, 0 = male) – – – – – – – – – – – – 2. Age 30.88 9.39 – −0.14 – – – – – – – – 3. Mental health symptoms (0–63) 37.50 20.18 0.93 0.05 −0.20* – – – – – – – 4. Resilience (1–5) 3.36 0.68 0.81 0.07 0.24** −0.33** – – – – – – 5. Self-compassion (1–5) 2.74 0.65 0.84 −0.04 0.36** −0.47** 0.53** – – – – – 6. Intrinsic motivation (4–28) 18.13 4.72 0.89 0.14 0.16 −0.01 0.25** 0.39** – – – – 7. Extrinsic motivation (4–28) 20.94 3.92 0.80 0.16 −0.17 0.05 0.05 0.01 0.62** – – – 8. Amotivation (4–28) 7.17 3.70 0.87 −0.06 −0.02 0.17 −0.21* −0.24** −0.21* −0.08 – – 9. Engagement (0–6) 3.77 0.09 0.93 0.10 0.28** −0.27** 0.34** 0.49** 0.59** 0.20* −0.45** – Variables M SD α 1 2 3 4 5 6 7 8 9 1. Gender(1 = female, 0 = male) – – – – – – – – – – – – 2. Age 30.88 9.39 – −0.14 – – – – – – – – 3. Mental health symptoms (0–63) 37.50 20.18 0.93 0.05 −0.20* – – – – – – – 4. Resilience (1–5) 3.36 0.68 0.81 0.07 0.24** −0.33** – – – – – – 5. Self-compassion (1–5) 2.74 0.65 0.84 −0.04 0.36** −0.47** 0.53** – – – – – 6. Intrinsic motivation (4–28) 18.13 4.72 0.89 0.14 0.16 −0.01 0.25** 0.39** – – – – 7. Extrinsic motivation (4–28) 20.94 3.92 0.80 0.16 −0.17 0.05 0.05 0.01 0.62** – – – 8. Amotivation (4–28) 7.17 3.70 0.87 −0.06 −0.02 0.17 −0.21* −0.24** −0.21* −0.08 – – 9. Engagement (0–6) 3.77 0.09 0.93 0.10 0.28** −0.27** 0.34** 0.49** 0.59** 0.20* −0.45** – * p < 0.05; **p < 0.01 (two-tailed). Open in new tab Table 1 Descriptive statistics and correlations among demographics, mental health symptoms, resilience, self-compassion, motivation, engagement in 116 UK social work students Variables M SD α 1 2 3 4 5 6 7 8 9 1. Gender(1 = female, 0 = male) – – – – – – – – – – – – 2. Age 30.88 9.39 – −0.14 – – – – – – – – 3. Mental health symptoms (0–63) 37.50 20.18 0.93 0.05 −0.20* – – – – – – – 4. Resilience (1–5) 3.36 0.68 0.81 0.07 0.24** −0.33** – – – – – – 5. Self-compassion (1–5) 2.74 0.65 0.84 −0.04 0.36** −0.47** 0.53** – – – – – 6. Intrinsic motivation (4–28) 18.13 4.72 0.89 0.14 0.16 −0.01 0.25** 0.39** – – – – 7. Extrinsic motivation (4–28) 20.94 3.92 0.80 0.16 −0.17 0.05 0.05 0.01 0.62** – – – 8. Amotivation (4–28) 7.17 3.70 0.87 −0.06 −0.02 0.17 −0.21* −0.24** −0.21* −0.08 – – 9. Engagement (0–6) 3.77 0.09 0.93 0.10 0.28** −0.27** 0.34** 0.49** 0.59** 0.20* −0.45** – Variables M SD α 1 2 3 4 5 6 7 8 9 1. Gender(1 = female, 0 = male) – – – – – – – – – – – – 2. Age 30.88 9.39 – −0.14 – – – – – – – – 3. Mental health symptoms (0–63) 37.50 20.18 0.93 0.05 −0.20* – – – – – – – 4. Resilience (1–5) 3.36 0.68 0.81 0.07 0.24** −0.33** – – – – – – 5. Self-compassion (1–5) 2.74 0.65 0.84 −0.04 0.36** −0.47** 0.53** – – – – – 6. Intrinsic motivation (4–28) 18.13 4.72 0.89 0.14 0.16 −0.01 0.25** 0.39** – – – – 7. Extrinsic motivation (4–28) 20.94 3.92 0.80 0.16 −0.17 0.05 0.05 0.01 0.62** – – – 8. Amotivation (4–28) 7.17 3.70 0.87 −0.06 −0.02 0.17 −0.21* −0.24** −0.21* −0.08 – – 9. Engagement (0–6) 3.77 0.09 0.93 0.10 0.28** −0.27** 0.34** 0.49** 0.59** 0.20* −0.45** – * p < 0.05; **p < 0.01 (two-tailed). Open in new tab Predictors of mental health Multiple regression analyses were conducted to identify independent predictors of mental health symptoms. At Step 1, age was entered to adjust for its effects (Table 1), and at Step 2, the scores for resilience, self-compassion, motivation and engagement were entered. Though uncorrelated, motivation variables were entered to determine more accurate regression coefficients (Pandey and Elliott, 2010). Because of the many predictor variables, adjusted coefficients of determination (adjusted R2) were reported. Multicollinearity was not a concern (all VIFs < 10). Resilience, self-compassion, motivation and engagement predicted 23 per cent of the variance for mental health symptoms (Table 2). Self-compassion was a positive predictor, and intrinsic motivation was a negative predictor of mental health. Resilience, extrinsic motivation, amotivation and engagement were not identified as independent predictors. H2 was not supported. Table 2 Multiple regression: resilience, self-compassion, motivation and engagement to mental health symptoms in 116 UK social work students Variables Mental health symptoms B SEB β Step 1  Age −0.07 0.03 −0.20*  Adjusted R2 – 0.03 – Step 2  Age −0.02 0.03 −0.05  Resilience −0.67 0.69 −0.09  Self-compassion −2.38 0.56 −0.46**  Intrinsic motivation 2.36 0.75 0.42**  Extrinsic motivation −1.23 0.84 −0.17  Amotivation 0.18 0.48 0.03  Engagement −2.34 1.41 −0.20  Δ Adjusted R2 0.23 Variables Mental health symptoms B SEB β Step 1  Age −0.07 0.03 −0.20*  Adjusted R2 – 0.03 – Step 2  Age −0.02 0.03 −0.05  Resilience −0.67 0.69 −0.09  Self-compassion −2.38 0.56 −0.46**  Intrinsic motivation 2.36 0.75 0.42**  Extrinsic motivation −1.23 0.84 −0.17  Amotivation 0.18 0.48 0.03  Engagement −2.34 1.41 −0.20  Δ Adjusted R2 0.23 B = unstandardised regression coefficient; SEB = standard error of the coefficient; β = standardised coefficient. * p < 0.05. ** p <0.01. Open in new tab Table 2 Multiple regression: resilience, self-compassion, motivation and engagement to mental health symptoms in 116 UK social work students Variables Mental health symptoms B SEB β Step 1  Age −0.07 0.03 −0.20*  Adjusted R2 – 0.03 – Step 2  Age −0.02 0.03 −0.05  Resilience −0.67 0.69 −0.09  Self-compassion −2.38 0.56 −0.46**  Intrinsic motivation 2.36 0.75 0.42**  Extrinsic motivation −1.23 0.84 −0.17  Amotivation 0.18 0.48 0.03  Engagement −2.34 1.41 −0.20  Δ Adjusted R2 0.23 Variables Mental health symptoms B SEB β Step 1  Age −0.07 0.03 −0.20*  Adjusted R2 – 0.03 – Step 2  Age −0.02 0.03 −0.05  Resilience −0.67 0.69 −0.09  Self-compassion −2.38 0.56 −0.46**  Intrinsic motivation 2.36 0.75 0.42**  Extrinsic motivation −1.23 0.84 −0.17  Amotivation 0.18 0.48 0.03  Engagement −2.34 1.41 −0.20  Δ Adjusted R2 0.23 B = unstandardised regression coefficient; SEB = standard error of the coefficient; β = standardised coefficient. * p < 0.05. ** p <0.01. Open in new tab Discussion This study investigated the relationships between mental health, resilience, self-compassion, motivation and engagement in UK social work students. H1 was partially supported: mental health symptoms were negatively related to resilience, self-compassion and engagement; however, they were not related to any type of motivation. H2 was not supported: only self-compassion was identified as a negative independent predictor of mental health symptoms. Contrary to H2, intrinsic motivation was identified as a positive independent predictor of mental health symptoms: resilience, extrinsic motivation, amotivation and engagement did not predict mental health symptoms. We will discuss each finding in turn, considering education for social work students. Our correlational analyses largely concurred with previous findings, revealing that resilience, self-compassion and engagement were strongly, negatively related to mental health symptoms: the more resilient, self-compassionate and engaged a student was, the less mental health difficulties they had, indicating their robust relationships. Contrary to previous findings, intrinsic motivation was not related to mental health. The correlation coefficient between self-compassion and mental health symptoms (−0.47) was larger than the one between resilience and mental health symptoms (−0.33), albeit not significantly. This may highlight the characteristics of social work students: for example, the majority of students will become a social worker. Social work students have clearer, yet more limited choices of future professions than students of other subjects, such as business studies where students have relatively broader choices of employment (Kotera et al., 2019a). This may cause them to compare themselves against an ideal image of a social worker, because social work students need to meet the values and principles of social workers, requiring high levels of work in a wide range of demands: challenging injustice and discrimination, while maintaining trusted relationships with other professionals and service users (BASW, 2012). This may relate to high levels of self-criticism and shame about not meeting the standards (Kotera et al., 2019b). On the other hand, for example, business students have less clearly defined pathways to, and more choices of employment, giving them psychological leeway, which allows them to interpret that not meeting one set of criteria can be a matter of ‘fit’, rather than a lack of competency (Kotera et al., 2019a). Self-compassion may be more effective in reducing mental health symptoms derived from negative comparisons against the standards to be a social worker, than resilience. Alternatively, self-compassion has been related to health-related behaviours (Dunne et al., 2016) and it may be that self-compassion improves self-care in social work students. Social work students’ strong focus on meeting the standards to become a social worker may also be inferred from their higher extrinsic motivation than intrinsic motivation, and a strong relation between them (r = 0.62; Table 1). This may imply social work students’ unclear distinction between extrinsic motivation and intrinsic motivation: while they enjoy studying the subject (intrinsic motivation), they are also driven by external instruments (extrinsic motivation) such as a clear career pathway to social work. Although intrinsic motivation and extrinsic motivation are not mutually exclusive (Lepper et al., 2005), it may be worthwhile to explore changes in the levels of intrinsic and extrinsic motivation throughout their programme, because many students have chosen to study social work for internal satisfaction, e.g. helping people in need (BASW, 2018b). Consistent with previous findings from other populations, engagement in UK social work students was also negatively related to mental health symptoms, and positively related to resilience (Turner et al., 2017) and other positive psychological constructs (Montero-Marin et al., 2016). This is important because burnout, a contrasting construct to engagement, is common in caring professionals, and could have diverse negative consequences, such as ineffective coping skills (Schaufeli et al., 2009). In addition to UK social workers’ relatively high level of engagement (Table 1; 3.77 of 6), these strong correlations between engagement and other constructs are useful to better appraise previous findings about burnout, while noting the ongoing debate about distinction between lack of engagement and burnout (e.g. Leon et al., 2015). For example, McFadden (2015) investigated the subcategories of burnout—emotional exhaustion, depersonalisation and personal accomplishment. Emotional exhaustion and depersonalisation scores were higher in 1,359 UK social workers than those of other caring professionals, whereas they had higher personal accomplishment, suggesting that while they were aware of their achievements, they still felt exhausted and detached. Contrarily, our sample of UK social work students’ engagement was relatively high, and the subscales (vigour, dedication and absorption) were strongly interrelated (r > 0.60; p < 0.01). Additionally, Biggart et al. (2016) recommended that policy makers explicitly recognise and acknowledge the values of social work practice to help reduce burnout in social work. In contrast, Montero-Marin et al.’s (2016) study using the three clinical subtypes of burnout (frenetic, underchallenged and worn-out) suggested that the frenetic subtype, driven by their greater need to achieve goals, may be related to extrinsic motivation. Though their approaches were different, the importance of extrinsic motivation was highlighted in relation to burnout in these studies. In our sample, engagement was associated with extrinsic motivation, but not as strongly as intrinsic motivation and amotivation (Table 1). Future research should explore the mechanisms underpinning these relationships: for example, how enhancement of each type of motivation impacts burnout and engagement of social workers and students. Multiple regression analyses (Table 2) identified that self-compassion and intrinsic motivation were independent predictors of mental health symptoms; self-compassion was a negative predictor and intrinsic motivation was a positive predictor. Since intrinsic motivation was a suppressor, it decreased outcome-irrelevant variation in the predictors (Pandey and Elliott, 2010); accordingly, it enhanced the accuracy of the regression analyses and improved the predictive power of the model (Cohen et al., 2003). Contrary to our hypothesis (H2) and previous literature, resilience, extrinsic motivation, amotivation and engagement were not independent predictors of mental health symptoms. More surprisingly, novel in motivation research is the finding that intrinsic motivation positively predicted the variance in mental health symptoms. This suggests that the UK social work students’ strong passion for the subject may compromise their mental health. Social work students, who have high levels of shame and self-criticism (Kotera et al., 2019b), frequently compare themselves with high and diverse standards. Because they are highly passionate about the subject, when they compare themselves with the standards of social work, they may scrutinise their competencies harshly, thus deteriorate their mental health. Obsessive passion—uncontrollable compulsion to initiate a passionate activity—may be present in social work students, and this type of passion has been negatively related to well-being, while harmonious passion—balanced and favourable engagement with a passionate activity—has been positively related to well-being (Lalande et al., 2017) . Future research needs to explore the relationships between their mental health, self-criticism and intrinsic motivation, along with underlying passions, to understand the likely mechanisms of intrinsic motivation for mental health. Additionally, evaluating the effects of compassion training on this dynamic may be worthwhile because this training was effective for reducing shame and self-criticism (Gilbert and Procter, 2006). Self-compassion negatively and independently predicted mental health symptoms, whereas resilience did not. While resilience has been noted as central to maintain a good level of mental health in the social work field, our analyses revealed that self-compassion is likely to have a greater impact on mental health than resilience in students. Currently, policies in social work primarily focus on resilience, and thus endorse teaching targeting resilience in social work students. However, an over-emphasis of resilience can misinform, and potentially damage, students’ mental health (Gask, 2015). The word ‘resilience’ is widely used today, and many students misunderstand it: some believe resilience means good mental health, therefore, when they have a mental health problem, they may perceive themselves as not resilient, leading to a sense of shame and self-criticism (Gask, 2015). The over-use of the resilience concept has been noted, and its usefulness has been questioned (Grünewald and Warner, 2012; Piña López, 2015). Likewise, resilience research includes diverse outcomes and has yielded mixed results (Masedo et al., 2014), thus the definition of resilience needs to be refined (Southwick et al., 2014) along with its measurement (Cosco et al., 2017). For example, the contextual nature of resilience is often forgotten; one’s resilience can be acknowledged by recognising their personal and environmental contexts holistically (Southwick et al., 2014; McAllister and Lowe, 2011). Indeed, resilience is more strongly related to one’s social and physical environment than one’s individual characteristics (Ungar, 2011), which Masten (1994) called ‘resiliency’ as opposed to the ecological process of ‘resilience’. We can better understand one’s resilience by including family, community, culture and socio-economic factors, rather than solely measuring one’s personal attributes (Ungar, 2011). Educators, researchers, policy makers and students may need to be more careful of the word use of ‘resilience’. Equivalently, as recent studies noted the benefits of self-compassion in caring professions (Iacono, 2017; Gregory, 2015) and this study found it to be the best predictor of mental health, future research and educational practice should explore the effects of self-compassion education and training on mental health of UK social work students. For example, educating students about the concept of self-compassion may increase their self-compassion: what it means, its components (self-kindness, common humanity and mindfulness), self-awareness and a non-judgemental attitude (Rickers, 2012). Moreover, some techniques can be practised in a classroom or placement setting: breathing, meditation (such as mindfulness or loving–kindness meditation), compassionate imagery and letter-writing (Iacono, 2017). Additionally, irregular contexts including student induction, group tutoring sessions and inter-professional events would also be an appropriate context for students to focus on self-compassion. Relatedly, due to the contagious nature of compassion, this may have positive impacts on the institutions in which they study and work (Gilbert et al., 2018), creating a safe workplace for educators (Killian, 2008). At a more macro-level, the UK nation is undergoing a chaotic period: political attention focuses on Brexit, and social workers face increased burden with less support. UK social workers play a key role to ensure these items do not irremediably slip off the national agenda (Golightley and Holloway, 2019). Positive psychological perspectives rather than victimised perspectives are useful in unstable social contexts (Reza and Bromfield, 2019). Compassion may be one psychological resource that social work students can focus on developing, to thrive in this uncertainty. There are several limitations to this study. First, the sample size was relatively small although satisfied the required sample size based on power calculations (eighty-four; Faul et al., 2009). Secondly, the majority of our participants were female undergraduate students at only one institution in the UK, which, although similar to the majority of social work programmes in the UK, may limit the generalisability of our findings. At last, the causal direction of these psychological constructs has not been investigated. In the future, longitudinal data would help elucidate the temporal patterning of the observed relationships; for example, it may be that resilience is more important in experienced social workers, whereas self-compassion is more important in social work students (McAllister and Lowe, 2011). This may help educators develop appropriate curriculum or interventions to increase our understanding of causality. Conclusion Due to challenging mental health, resilience has been a focus on social work. Such a positive psychological construct may provide an alternative solution to mental health, as social work students often have high shame about mental health. This study evaluated the relationships between mental health and resilience and other positive psychological constructs—self-compassion, motivation and engagement. The results highlighted that, although resilience was associated with mental health symptoms, self-compassion was more strongly associated with those symptoms. Furthermore, while self-compassion independently predicted fewer mental health symptoms, resilience did not. The findings in this study suggest focusing on self-compassion and limiting the overuse and misunderstanding of ‘resilience’, particularly in relation to mental health in UK social work students. Future research should evaluate the benefits of self-compassion training on the mental health of social work students. UK social work policy makers, educators, researchers and students may need to re-focus the current emphasis on resilience to self-compassion and self-care in the social work education, in order to protect students’ mental health. 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