The Influence of Meaning in Life on Adolescents’ Hedonic Well-Being and Risk Behaviour: Implications for Social Work

The Influence of Meaning in Life on Adolescents’ Hedonic Well-Being and Risk Behaviour:... Abstract The strength-based approach argues that promoting adolescents’ developmental assets would contribute to their well-being. This study investigated meaning in life (MIL) as one of such developmental assets. Based on the annual assessment of 3,328 Hong Kong Chinese adolescents from Grade 7 to Grade 12, we found that MIL at Grade 7 predicted heightened hedonic well-being and reduced risk behaviour at Grade 12; slower decrease in MIL from Grade 7 to Grade 11 predicted heightened hedonic well-being and reduced risk behaviour at Grade 12 with the initial level of outcomes controlled. Findings highlight the importance of MIL as a developmental asset fostering adolescent well-being. Implications of the findings for advancing theory of MIL and social work practice are discussed. Meaning in life, hedonic well-being, risk behaviour, Chinese adolescents, longitudinal study Introduction Adolescents’ well-being is one of the primary concerns of social work and youth-helping professionals. Well-being refers to ‘the state of being happy, healthy, and prosperous’ (Black and Jeffery, 2007, p. 1084) and includes both the positive (e.g. life satisfaction) and negative indicators (e.g. behavioural problems; Lee, 2014). Teenagers face myriad transformations in multiple areas of their lives on their path to adulthood, including self, family, peer and school, which increase the challenges for sustaining well-being (Call et al., 2002). Research has shown that a significant proportion of teenagers experience stress and depression in their daily lives (APA, 2014) and display risk behaviour such as suicide attempts and non-suicidal self-harm (Kokkevi et al., 2012), as well as problematic internet use (Tsitsika et al., 2014). In Hong Kong, the report of the Hong Kong Mental Health Index Research Group (2014) also showed that the well-being index of young people declined to a level that was close to the level indicative of clinical depression. The strength-based approach (Lerner, 2004; Starnino et al., 2014) posits that promoting adolescents’ meaning in life (MIL) is one of the potential solutions to the vulnerability to dampened well-being in adolescence, because MIL is one of the spiritual strengths that help improve positive functioning and reduce psychological problems. However, the existing research on the role of MIL in the adolescent period is still inadequate, which hinders the theoretical advancement and practical improvement in promoting adolescent well-being. In the field of social work, there is a growing awareness of integrating clients’ spiritual strengths into social work theory and practice (Cheon and Canda, 2010; Starnino et al., 2014; George and Ellison, 2015). Spirituality refers to the search for connectedness, meaning, purpose and contribution from life experiences, and MIL is one of the essential components (Benson et al., 2003; Cheon and Canda, 2010). Spirituality can serve as an internal resource that facilitates the recovery of clients with mental problems (Starnino et al., 2014). As argued by Gardner (2017), ‘spirituality is part of each person’s experience and therefore needs to be seen as an integral part of practice’ (p. 305). Yet, such concern is largely limited to adult targets with very few social work studies examining the spiritual concerns of young people (Cheon and Canda, 2010). It is still debatable whether spirituality-related practices apply to adolescents, because some people regard them as ‘too young’ to think about and make good use of spirituality (see Fitzgerald, 2005). In the strength-based approach, spirituality has been regarded as a developmental asset that nurtures positive youth development (Benson et al., 2003). Cheon and Canda (2010) called for integrating spirituality into social work practice and other youth-helping activities with reference to the strength-based approach. They argued that ‘it is critical for social workers to increase their awareness of their young clients’ spirituality and whether it is relevant to practice goals in child welfare, school social work, family services, and other youth-serving contexts’ (p. 123). Unfortunately, related empirical evidence that examines adolescents’ spiritual development such as the developmental change in MIL is sparse. To advance the theoretical formulation of spiritual development and spirituality-related practices in youth, we need more empirical evidence showing the benefits of promoting MIL in adolescents. Therefore, this study aimed at understanding the role of MIL in multiple markers of adolescent well-being with a six-wave longitudinal design. The findings of the current study would have implications for advancing theoretical understanding on the role of MIL in youth development as well as innovation of social work practice addressing adolescent well-being. MIL and adolescent well-being MIL was defined as ‘ontological significance of life from the view of the experiencing individual’ (Crumbaugh and Maholick, 1964, p. 185). People who have this subjective judgement and experience that ‘life is meaningful’ are able to make sense of the life events, see the significance of their lives and establish an enduring purpose that directs their lives (Steger et al., 2009). In youth development, the strength-based approach regards MIL as a developmental asset, which refers to positive experiences and qualities that direct adolescents away from risk behaviour, foster their resilience and promote their thriving (Benson et al., 2003; Benson and Scales, 2011). MIL may foster a happy life. MIL is regarded as an indicator of eudaimonic well-being that entails fulfilment of human potentials and personal growth (Ryff, 1989), which is different from hedonic well-being that refers to the presence of happiness and contentment (Baumeister et al., 2013). A meaningful life is not equal to a happy life, but possible to foster a happy life. If one interprets the events in his/her life in a meaningful and coherent way, he/she understands that his/her life holds value, which is presumably related to positive outlook towards life (Steger, 2012). Several studies found that adolescents who reported greater MIL had higher life satisfaction (Ho et al., 2010), happiness (Kiang and Fuligni, 2010) and sense of mastery (Shek, 2001), as well as lower anxiety (Shek, 1992), depression (Ho et al., 2010) and hopelessness (Brassai et al., 2012). On top of that, MIL may protect adolescents from risk behaviour (Brassai et al., 2011; Zhang et al., 2015). According to Frankl’s (1959) theory, questing for MIL is a fundamental human motive, which was called ‘will to meaning’. People who fail to fulfil the MIL may experience ‘existential vacuum’ (existential frustration) and consequently psychological problems would occur to fill this vacuum. The lack of life meaning is manifested in a feeling of boredom and apathy (Maddi, 1967; Fahlman et al., 2009). Boredom as a loss of interest in life may render one to engage in activities that are sensuously exciting, such as breaking the rules or norms (Wegner and Flisher, 2009) or indulging oneself in the virtual world (Lin et al., 2009). Apathy as a loss of motivation to take action is a symptom of depression, which can also trigger risk behaviour such as suicidal attempt or non-suicidal self-harm (Kandel et al., 1991; Jacobson and Gould, 2007). Frankl’s theory has been supported by a handful of studies. For example, Brassai et al. (2011)’s study found that MIL was negatively related to binge drinking, unsafe sex and lack of exercises and diet control among female adolescents, and to illicit drug and sedative use among male adolescents. Despite the growing evidence showing positive psychological correlates of MIL in adolescents, the argument that MIL is the foundation of a better life (Frankl, 1959; Steger, 2012) remains a conjecture only. Specifically, can MIL serve as a foundation that produces happiness and keeps adolescents away from risk behaviour? To answer this question, we need evidence supporting that MIL is predictive of hedonic well-being and risk behaviour, and the effects are long-lasting. Yet, the literature largely based on the concurrent associations between MIL and other well-being indicators (e.g. Steger et al., 2009; Kiang and Witkow, 2015) provides meagre evidence for this question. The cross-sectional design, even panel data without including time-order effects (Kiang and Witkow, 2015), informs little about the directionality of association (e.g. can MIL predict hedonic well-being?) and fails to confirm whether the meaningful experience simply elicits instant good feelings or produces enduring benefits (i.e. would MIL have a long-term effect on hedonic well-being?). Theoretically, adolescents’ spiritual development is a powerful resource that nurtures positive youth development (Benson et al., 2003). Additionally, Baumeister et al. (2013) argued that meaning is not subject to the present moment, but goes beyond the here and now, and thus it may have a more enduring effect. Empirically, to validate these arguments, a longitudinal design that tests the time-order effect of MIL on succeeding outcomes is highly needed. So far, there is only one direct piece of evidence (Shek, 1998), which found that MIL predicted decreased psychological disturbance one year later among adolescents. To fill the gap between the theoretical argument for the importance of MIL in human welfare (particularly adolescent welfare) and the potent empirical support, we certainly need more longitudinal studies that extend the time frame and include other well-being indicators. Additionally, existing theories of MIL seldom address the dynamic nature of MIL. Much of the research assumed MIL as a relatively stable and trait-like attribute about the degree to which one perceives his/her life to be meaningful (e.g. Ho et al., 2010). However, during secondary-school years, adolescents’ MIL dropped despite a minor rebound in Grade 11 (Shek and Lin, 2017). In addition to the simple level at one time point, many studies have found that how a developmental attribute changes over time has additional psychological implications (e.g. Wang and Pomerantz, 2009). While lacking a sense of meaning is harmful, experiencing a decline in MIL across time may bring about an additional detrimental effect on adolescent well-being. The forefront of contemporary theories about youth development and social work practice concerns the ‘change’ in persons, with an objective to help individuals to change in a direction that fosters individual well-being and social goods (Benson et al., 2006). However, to our best knowledge, the empirical evidence about the change in MIL and its consequences is almost missing. Investigating the systematic change in MIL expands the scope of theoretical understanding on the role of MIL in well-being, which serves as an active response to the contemporary theory advancement. Altogether, to fill the gap regarding the theoretical understanding of the dynamic nature of MIL, we need studies that trace the change in MIL over time instead of focusing on the static level of MIL. Current study With regard to the aforementioned research gaps, the current study primarily aims at understanding how MIL influences different aspects of adolescent well-being using a developmental perspective. With a longitudinal design of six years of annual assessments, we address four research questions. The first two questions test whether the initial level of MIL and the change in MIL would predict adolescents’ hedonic well-being indexed by life satisfaction and hopelessness. There is evidence suggesting that MIL is positively related to life satisfaction (e.g. Ho et al., 2010) and negatively related to hopelessness among adolescents (Brassai et al., 2012) and young adults (Shek, 1993). Therefore, we hypothesised that MIL at Grade 7 would predict increased life satisfaction and decreased hopelessness at Grade 12 (Hypotheses 1a and 1b). Moreover, according to the literature reviewed above, it is reasonable to believe that a declining trend of MIL indicates poor hedonic well-being in adolescents. Therefore, we hypothesised that the faster rate of decline in MIL from Grades 7 to 11 would predict decreased life satisfaction and increased hopelessness at Grade 12 (Hypotheses 2a and 2b). The last two questions regard whether the initial level of MIL and the change in MIL would predict risk behaviour among adolescents. We examined adolescents’ risk behaviour indexed by delinquent behaviour, problematic internet use, non-suicidal self-harm behaviour and suicidal behaviour. The most obvious connection is the negative relationship between MIL and suicide, since they look incompatible (Kleiman and Beaver, 2013). Previous research on adults has established the link between MIL and suicide (Kleiman and Beaver, 2013). Furthermore, one previous piece of research revealed the inverse relationship between MIL and delinquent behaviour among adolescents (Shek et al., 1994). The presence of life meaning was also inversely related to self-harm behaviour (Kress et al., 2015) and internet addiction (Zhang et al., 2015) among young adults. We thus expected that MIL at Grade 7 would predict decreased delinquent behaviour, problematic internet use, non-suicidal self-harm behaviour and suicidal behaviour at Grade 12 (Hypotheses 3a–3d). Similarly, we also expected that the faster rate of decline in MIL from Grades 7 to 11 would predict increased risk behaviours at Grade 12 (Hypotheses 4a–4d). We also explored the potential role of gender in the developmental change in MIL and in the effect of MIL. If questing for meaning is a fundamental human motive, the presence of MIL should be equally important for females and males, which is evident in some previous studies (e.g. Shek, 1998) but not the others (e.g. Brassai et al., 2011). Due to the inconsistent results, we explored whether the change in MIL and its effects would vary as a function of gender. Method Participants and procedure Participants were from the Project P.A.T.H.S. (i.e. Positive Adolescent Training through Holistic Social Programmes), a six-year longitudinal study in Hong Kong. Project P.A.T.H.S. consisted of annual assessments over six years among high-school students from Grade 7 to Grade 12 (Shek and Lin, 2017). At Grade 7, a total of 3,328 students joined the project (Mean age = 12.59 ± 0.74 years; 51.7 per cent male). The attrition rates ranged from 12.7 per cent to 28.3 per cent due to students’ drop-out, transferring schools or absence on the day of assessment. The interval between adjacent waves was about one year except for that between Wave 5 and Wave 6 (i.e. ten months), as the students needed to sit the public entrance examination for college. Table 1 shows the detailed characteristics of the participants. Table 1 Descriptive information about the participants and variable of MIL Grade 7 % Grade 8 % Grade 9 % Grade 10 % Grade 11 % Grade 12 % N (participants) 3,328 2,905 2,860 2,684 2,474 2,385 Average age 12.59 13.59 14.54 15.50 16.37 17.20 Gender Male 1,719 51.7 1,445 49.7 1,424 49.8 1,323 49.3 1,187 48.0 1,144 48.0 Female 1,572 47.2 1,429 49.2 1,406 49.2 1,330 49.6 1,258 50.8 1,211 50.8 Economic disadvantage  NOT receiving CSSA 2,606 78.3 2,309 79.5 2,290 80.1 2,144 79.9 1,985 80.2 1,913 80.2  Receiving CSSA 225 6.8 191 6.6 184 6.4 178 6.6 159 6.4 157 6.6 Family intactness  Intact families 2,781 83.6 2,446 84.2 2,418 84.5 2,283 85.1 2,097 84.8 2,029 85.1  Non-intact families 515 15.5 432 14.9 418 14.6 379 14.1 355 14.3 335 14.0 Meaning in life Mean 5.14 5.01 5.01 4.96 4.92 SD 1.32 1.29 1.26 1.23 1.24 α 0.88 0.89 0.90 0.92 0.91 Inter-item# 0.71 0.74 0.76 0.78 0.78 Grade 7 % Grade 8 % Grade 9 % Grade 10 % Grade 11 % Grade 12 % N (participants) 3,328 2,905 2,860 2,684 2,474 2,385 Average age 12.59 13.59 14.54 15.50 16.37 17.20 Gender Male 1,719 51.7 1,445 49.7 1,424 49.8 1,323 49.3 1,187 48.0 1,144 48.0 Female 1,572 47.2 1,429 49.2 1,406 49.2 1,330 49.6 1,258 50.8 1,211 50.8 Economic disadvantage  NOT receiving CSSA 2,606 78.3 2,309 79.5 2,290 80.1 2,144 79.9 1,985 80.2 1,913 80.2  Receiving CSSA 225 6.8 191 6.6 184 6.4 178 6.6 159 6.4 157 6.6 Family intactness  Intact families 2,781 83.6 2,446 84.2 2,418 84.5 2,283 85.1 2,097 84.8 2,029 85.1  Non-intact families 515 15.5 432 14.9 418 14.6 379 14.1 355 14.3 335 14.0 Meaning in life Mean 5.14 5.01 5.01 4.96 4.92 SD 1.32 1.29 1.26 1.23 1.24 α 0.88 0.89 0.90 0.92 0.91 Inter-item# 0.71 0.74 0.76 0.78 0.78 Participants who did not provide demographic information were not included. #Mean of inter-item correlations. Table 1 Descriptive information about the participants and variable of MIL Grade 7 % Grade 8 % Grade 9 % Grade 10 % Grade 11 % Grade 12 % N (participants) 3,328 2,905 2,860 2,684 2,474 2,385 Average age 12.59 13.59 14.54 15.50 16.37 17.20 Gender Male 1,719 51.7 1,445 49.7 1,424 49.8 1,323 49.3 1,187 48.0 1,144 48.0 Female 1,572 47.2 1,429 49.2 1,406 49.2 1,330 49.6 1,258 50.8 1,211 50.8 Economic disadvantage  NOT receiving CSSA 2,606 78.3 2,309 79.5 2,290 80.1 2,144 79.9 1,985 80.2 1,913 80.2  Receiving CSSA 225 6.8 191 6.6 184 6.4 178 6.6 159 6.4 157 6.6 Family intactness  Intact families 2,781 83.6 2,446 84.2 2,418 84.5 2,283 85.1 2,097 84.8 2,029 85.1  Non-intact families 515 15.5 432 14.9 418 14.6 379 14.1 355 14.3 335 14.0 Meaning in life Mean 5.14 5.01 5.01 4.96 4.92 SD 1.32 1.29 1.26 1.23 1.24 α 0.88 0.89 0.90 0.92 0.91 Inter-item# 0.71 0.74 0.76 0.78 0.78 Grade 7 % Grade 8 % Grade 9 % Grade 10 % Grade 11 % Grade 12 % N (participants) 3,328 2,905 2,860 2,684 2,474 2,385 Average age 12.59 13.59 14.54 15.50 16.37 17.20 Gender Male 1,719 51.7 1,445 49.7 1,424 49.8 1,323 49.3 1,187 48.0 1,144 48.0 Female 1,572 47.2 1,429 49.2 1,406 49.2 1,330 49.6 1,258 50.8 1,211 50.8 Economic disadvantage  NOT receiving CSSA 2,606 78.3 2,309 79.5 2,290 80.1 2,144 79.9 1,985 80.2 1,913 80.2  Receiving CSSA 225 6.8 191 6.6 184 6.4 178 6.6 159 6.4 157 6.6 Family intactness  Intact families 2,781 83.6 2,446 84.2 2,418 84.5 2,283 85.1 2,097 84.8 2,029 85.1  Non-intact families 515 15.5 432 14.9 418 14.6 379 14.1 355 14.3 335 14.0 Meaning in life Mean 5.14 5.01 5.01 4.96 4.92 SD 1.32 1.29 1.26 1.23 1.24 α 0.88 0.89 0.90 0.92 0.91 Inter-item# 0.71 0.74 0.76 0.78 0.78 Participants who did not provide demographic information were not included. #Mean of inter-item correlations. Participants completed a battery of questionnaires in a classroom setting during school hours with the presence of a trained research assistant. This study has obtained ethical approval from the Human Subjects Ethics Sub-committee of The Hong Kong Polytechnic University. Parental consent and school consent were obtained before the administration of the first wave of assessment and students’ consent was obtained at the beginning of every wave of assessment. Instruments Participants reported on MIL, hedonic well-being and risk behaviour using self-administrated questionnaires. The scales used have all been successfully adopted in previous studies on Chinese adolescents (e.g. Shek, 2004) and showed acceptable internal consistencies in this study. Tables 1 and 2 show the descriptive information about the study variables. Table 2 Descriptive information of the outcome variables Grade 7 Grade 12 Grade 7 to Grade 12 Variable Mean SD α Inter- item# Mean SD α Inter- item# r Life satisfaction 3.95 1.11 0.85 0.55 3.60 1.05 0.88 0.62 0.372 Hopelessness 2.68 1.18 0.85 0.55 2.66 1.07 0.89 0.63 0.330 Delinquent behaviour 0.39 0.47 0.70 0.25 0.49 0.55 0.69 0.29 0.348 Problematic internet use 0.23 0.24 0.79 0.28 0.17 0.22 0.81 0.32 0.227 Deliberate self-harm behaviour 0.04 0.10 0.83 0.25 0.02 0.08 0.86 0.40 0.083 Suicidal behaviour 0.08 0.21 0.68 0.44 0.05 0.16 0.61 0.40 0.137 Grade 7 Grade 12 Grade 7 to Grade 12 Variable Mean SD α Inter- item# Mean SD α Inter- item# r Life satisfaction 3.95 1.11 0.85 0.55 3.60 1.05 0.88 0.62 0.372 Hopelessness 2.68 1.18 0.85 0.55 2.66 1.07 0.89 0.63 0.330 Delinquent behaviour 0.39 0.47 0.70 0.25 0.49 0.55 0.69 0.29 0.348 Problematic internet use 0.23 0.24 0.79 0.28 0.17 0.22 0.81 0.32 0.227 Deliberate self-harm behaviour 0.04 0.10 0.83 0.25 0.02 0.08 0.86 0.40 0.083 Suicidal behaviour 0.08 0.21 0.68 0.44 0.05 0.16 0.61 0.40 0.137 All the correlations (r) were significant. #Mean of inter-item correlations. Table 2 Descriptive information of the outcome variables Grade 7 Grade 12 Grade 7 to Grade 12 Variable Mean SD α Inter- item# Mean SD α Inter- item# r Life satisfaction 3.95 1.11 0.85 0.55 3.60 1.05 0.88 0.62 0.372 Hopelessness 2.68 1.18 0.85 0.55 2.66 1.07 0.89 0.63 0.330 Delinquent behaviour 0.39 0.47 0.70 0.25 0.49 0.55 0.69 0.29 0.348 Problematic internet use 0.23 0.24 0.79 0.28 0.17 0.22 0.81 0.32 0.227 Deliberate self-harm behaviour 0.04 0.10 0.83 0.25 0.02 0.08 0.86 0.40 0.083 Suicidal behaviour 0.08 0.21 0.68 0.44 0.05 0.16 0.61 0.40 0.137 Grade 7 Grade 12 Grade 7 to Grade 12 Variable Mean SD α Inter- item# Mean SD α Inter- item# r Life satisfaction 3.95 1.11 0.85 0.55 3.60 1.05 0.88 0.62 0.372 Hopelessness 2.68 1.18 0.85 0.55 2.66 1.07 0.89 0.63 0.330 Delinquent behaviour 0.39 0.47 0.70 0.25 0.49 0.55 0.69 0.29 0.348 Problematic internet use 0.23 0.24 0.79 0.28 0.17 0.22 0.81 0.32 0.227 Deliberate self-harm behaviour 0.04 0.10 0.83 0.25 0.02 0.08 0.86 0.40 0.083 Suicidal behaviour 0.08 0.21 0.68 0.44 0.05 0.16 0.61 0.40 0.137 All the correlations (r) were significant. #Mean of inter-item correlations. MIL Participants reported their feelings that life is meaningful via the three-item subscale of spirituality on a seven-point Likert scale (e.g. life is empty or exciting). This scale is derived from subscale of spirituality of the short-version Chinese Positive Youth Development Scale (CPYDS, Shek et al., 2007). This subscale was originally adapted from the Purpose in Life Questionnaire (Crumbaugh and Maholick, 1964; Shek et al., 1987), which has been successfully used in Chinese adolescent samples (Shek, 1992, 2001). Well-being The five-item translated version of the Satisfaction with Life Scale (Diener et al., 1985; Shek, 2004) was used to measure participants’ overall evaluation of their life satisfaction using a six-point Likert scale (e.g. one is satisfied with life). Adapted from the Hopelessness Scale (Beck et al., 1974), the five-item Chinese Hopelessness Scale (Shek, 1993) was used to measure participants’ sense of hopelessness using a six-point Likert scale (e.g. future seems gloomy). Risk behaviour Delinquent behaviour: Participants reported their frequency of engagement in twelve delinquent acts in the past year, including cheating, stealing, running away from home, truancy, damaging others’ properties, speaking foul language, assault, gang fighting, having sexual intercourse with others, trespassing behaviour on a seven-point scale (0 = never, 6 = more than ten times; Shek, 2004). Problematic internet use: Young’s ten-item Internet Addiction Test (IAT) (Young, 1998), which has been validated in the Chinese context (Shek et al., 2008), was adopted to measure participants’ problematic internet use. Participants answered yes or no to ten addictive symptoms (e.g. staying longer online than one intended) in the past year. We used mean scores across ten items to indicate the degree of the problematic internet use. Deliberate self-harm: A checklist of seventeen forms of deliberate self-harm behaviour without suicidal ideation was used to measure participants’ occurrence of deliberate self-harm behaviour (Shek and Yu, 2012). The participants reported whether they had engaged in these behaviours in the past year, such as wrist cutting, self-scratching, carving words on the body (1= yes, 0 = no). Suicidal behaviour: A three-item checklist was used to measure participants’ suicidal ideation, suicidal plan and suicidal attempt in the past year, respectively (1 = yes, 0 = no) (Shek and Yu, 2012). Data-analysis plan The current study primarily tested whether MIL and its change would predict adolescent well-being over time. Latent growth curve (LGC) modelling was used via AMOS 22.0 to address the questions with full information maximum likelihood to address the incomplete data. LGC modelling allows researchers to capture the intra-individual change of psychological attribute over time and its relations to other variables (Duncan et al., 1999). Therefore, it allows us to test how the change in MIL affects adolescent well-being. First, we used LGC to establish the developmental trajectory of MIL across five years. Next, we used extended LGC modelling to test the associations between MIL and the indicators of adolescent well-being. As the effects might vary across different indicators, each model estimated one outcome variable. As shown in Figure 1, each model consisted of two latent factors that could be correlated. By specifying the factor loadings from the ‘intercept’ to the MIL variables assessed at the five waves as 1, the latent factor of intercept indicates the average level of MIL at the initial wave. By specifying the factor loadings from the ‘slope’ to the MIL variable assessed at the five waves as 0, 1, 2, 3 and 4, respectively, the latent factor of the slope indicates the rate of change in MIL across the five waves over high-school years. The effect of MIL at the initial wave (Grade 7) on the well-being or risk behaviour at Grade 12 was estimated as the ‘intercept effect’ and the effect of change in MIL (from Grades 7 to 11) on well-being or risk behaviour at Grade 12 was estimated as the ‘slope effect’. These effects were estimated with the temporal stability of the outcome variable controlled. Additionally, as previous studies have suggested that economic status and family intactness might affect the outcome variables (Shek and Lin, 2015, 2016), we included them as control variables. Finally, we explored gender differences in these models by comparing the model with intercept effect and slope effect constrained to be equal across the male and female groups with the unconstrained model (i.e. multi-group analysis). We used CFI, TLI (>0.90 acceptable fit; >0.95 good fit) and RMSEA (<0.08 acceptable fit; <0.05 good fit) for model fit (Byrne, 2001). Figure 1 View largeDownload slide A schematic illustration of the extended LGC model. MIL, meaning in life. Demographic variables were also controlled in this model. Error terms of observed variables are omitted in the figure. Figure 1 View largeDownload slide A schematic illustration of the extended LGC model. MIL, meaning in life. Demographic variables were also controlled in this model. Error terms of observed variables are omitted in the figure. Results Change in MIL Using LGC modelling, we found that MIL decreased over time (B = –0.105, SE = 0.019, p < 0.001), but the decrease rate slowed down (B = 0.011, SE = 0.004, p = 0.010) with gender, economic status and family intactness as control variables. In addition, females (B = –0.054, SE = 0.023, p = 0.019), non-poor ones (B = –0.046, SE = 0.013, p < 0.001) and those living in intact families (B = 0.097, SE = 0.017, p < 0.001) reported higher levels of MIL at the initial assessment than males, poor ones and those living in non-intact families. However, those from intact families experienced a slightly faster decline in MIL (B = –0.029, SE = 0.014, p = 0.031). The model had a good fit (CFI = 0.995, TLI = 0.986, RMSEA = 0.026). As the high-school period was dominated by the declining trend with a small rebound in Grade 11, we only included the linear change in the following extended LGC models. Intercept effects and slope effects Bivariate correlations provided information on the unadjusted associations between MIL and adolescent well-being. The correlations (see Table 3) indicated that the initial MIL was significantly associated with life satisfaction, hopelessness, delinquent behaviour, problematic internet use, deliberate self-harm behaviour and suicidal behaviour at Grade 7 and those at Grade 12. Additionally, the levels of MIL across the five waves were significantly associated with these outcome variables. Table 3 Bivariate correlations of outcome variables (Grade 12) and their predictors Life satisfaction Hopelessness Delinquent behaviour Problematic internet use Deliberate self-harm behaviour Suicidal behaviour Meaning in life Grade 7 0.276 –0.287 –0.100 –0.103 –0.054 –0.136 (0.604) (–0.459) (–0.263) (–0.281) (–0.243) (–0.291) Grade 8 0.322 –0.364 –0.106 –0.099 –0.059 –0.147 Grade 9 0.397 –0.381 –0.074 –0.107 –0.094 –0.161 Grade 10 0.441 –0.443 –0.116 –0.141 –0.099 –0.215 Grade 11 0.488 –0.443 –0.116 –0.141 –0.099 –0.215 Demographic variables (Grade 7) Gender (0 = female, 1 = male) –0.042 0.103 0.168 0.042 0.001 –0.011 Economic status (0 = non-poor, 1 = poor) –0.070 0.054 0.028 0.038 0.049 0.046 Family intactness (0 = non-intact family, 1 = intact family) 0.048 –0.041 –0.052 –0.010 –0.053 –0.075 Life satisfaction Hopelessness Delinquent behaviour Problematic internet use Deliberate self-harm behaviour Suicidal behaviour Meaning in life Grade 7 0.276 –0.287 –0.100 –0.103 –0.054 –0.136 (0.604) (–0.459) (–0.263) (–0.281) (–0.243) (–0.291) Grade 8 0.322 –0.364 –0.106 –0.099 –0.059 –0.147 Grade 9 0.397 –0.381 –0.074 –0.107 –0.094 –0.161 Grade 10 0.441 –0.443 –0.116 –0.141 –0.099 –0.215 Grade 11 0.488 –0.443 –0.116 –0.141 –0.099 –0.215 Demographic variables (Grade 7) Gender (0 = female, 1 = male) –0.042 0.103 0.168 0.042 0.001 –0.011 Economic status (0 = non-poor, 1 = poor) –0.070 0.054 0.028 0.038 0.049 0.046 Family intactness (0 = non-intact family, 1 = intact family) 0.048 –0.041 –0.052 –0.010 –0.053 –0.075 The numbers in the parentheses were the correlations between meaning in life and outcome variables at Grade 7; |r| larger than 0.041 was significant at p < 0.05. Table 3 Bivariate correlations of outcome variables (Grade 12) and their predictors Life satisfaction Hopelessness Delinquent behaviour Problematic internet use Deliberate self-harm behaviour Suicidal behaviour Meaning in life Grade 7 0.276 –0.287 –0.100 –0.103 –0.054 –0.136 (0.604) (–0.459) (–0.263) (–0.281) (–0.243) (–0.291) Grade 8 0.322 –0.364 –0.106 –0.099 –0.059 –0.147 Grade 9 0.397 –0.381 –0.074 –0.107 –0.094 –0.161 Grade 10 0.441 –0.443 –0.116 –0.141 –0.099 –0.215 Grade 11 0.488 –0.443 –0.116 –0.141 –0.099 –0.215 Demographic variables (Grade 7) Gender (0 = female, 1 = male) –0.042 0.103 0.168 0.042 0.001 –0.011 Economic status (0 = non-poor, 1 = poor) –0.070 0.054 0.028 0.038 0.049 0.046 Family intactness (0 = non-intact family, 1 = intact family) 0.048 –0.041 –0.052 –0.010 –0.053 –0.075 Life satisfaction Hopelessness Delinquent behaviour Problematic internet use Deliberate self-harm behaviour Suicidal behaviour Meaning in life Grade 7 0.276 –0.287 –0.100 –0.103 –0.054 –0.136 (0.604) (–0.459) (–0.263) (–0.281) (–0.243) (–0.291) Grade 8 0.322 –0.364 –0.106 –0.099 –0.059 –0.147 Grade 9 0.397 –0.381 –0.074 –0.107 –0.094 –0.161 Grade 10 0.441 –0.443 –0.116 –0.141 –0.099 –0.215 Grade 11 0.488 –0.443 –0.116 –0.141 –0.099 –0.215 Demographic variables (Grade 7) Gender (0 = female, 1 = male) –0.042 0.103 0.168 0.042 0.001 –0.011 Economic status (0 = non-poor, 1 = poor) –0.070 0.054 0.028 0.038 0.049 0.046 Family intactness (0 = non-intact family, 1 = intact family) 0.048 –0.041 –0.052 –0.010 –0.053 –0.075 The numbers in the parentheses were the correlations between meaning in life and outcome variables at Grade 7; |r| larger than 0.041 was significant at p < 0.05. Table 4 presents the results of extended LGC models based on the whole sample. Regarding hedonic well-being, the initial levels of MIL predicted heightened life satisfaction and lessened hopelessness at Grade 12, adjusting the initial levels of life satisfaction and hopelessness, respectively. Additionally, change in MIL positively predicted life satisfaction and negatively predicted hopelessness at Grade 12, adjusting their initial levels. In other words, the smaller the decrease in MIL over the first five school years, the higher the level of life satisfaction and the lower the level of hopelessness at the final year. Table 4 Results of extended LGC modelling Temporal stability Intercept effect Slope effect Model fit Unstd. SE Std. Unstd. SE Std. Unstd. SE Std. Δχ2/2 CFI TLI RMSEA Hedonic well-being  Life satisfaction 0.388c 0.030 0.406 0.345c 0.030 0.340 3.488c 0.148 0.678 21.245c 0.961 0.933 0.062  Hopelessness 0.238c 0.022 0.260 –0.484c 0.025 –0.464 –3.182c 0.144 –0.592 5.965 0.971 0.950 0.051 Risk behaviour  Delinquent behaviour 0.489c 0.026 0.408 –0.031a 0.013 –0.057 –0.245b 0.079 –0.086 0.16 0.972 0.952 0.047  Problematic internet use 0.223c 0.020 0.243 –0.023c 0.005 –0.107 –0.240c 0.032 –0.215 1.779 0.971 0.950 0.047  Self-harm behaviour 0.084c 0.019 0.105 –0.009c 0.002 –0.114 –0.085c 0.012 –0.214 1.579 0.970 0.949 0.048  Suicidal behaviour 0.109c 0.017 0.143 –0.036c 0.004 –0.240 –0.289c 0.023 –0.367 1.971 0.972 0.952 0.047 Temporal stability Intercept effect Slope effect Model fit Unstd. SE Std. Unstd. SE Std. Unstd. SE Std. Δχ2/2 CFI TLI RMSEA Hedonic well-being  Life satisfaction 0.388c 0.030 0.406 0.345c 0.030 0.340 3.488c 0.148 0.678 21.245c 0.961 0.933 0.062  Hopelessness 0.238c 0.022 0.260 –0.484c 0.025 –0.464 –3.182c 0.144 –0.592 5.965 0.971 0.950 0.051 Risk behaviour  Delinquent behaviour 0.489c 0.026 0.408 –0.031a 0.013 –0.057 –0.245b 0.079 –0.086 0.16 0.972 0.952 0.047  Problematic internet use 0.223c 0.020 0.243 –0.023c 0.005 –0.107 –0.240c 0.032 –0.215 1.779 0.971 0.950 0.047  Self-harm behaviour 0.084c 0.019 0.105 –0.009c 0.002 –0.114 –0.085c 0.012 –0.214 1.579 0.970 0.949 0.048  Suicidal behaviour 0.109c 0.017 0.143 –0.036c 0.004 –0.240 –0.289c 0.023 –0.367 1.971 0.972 0.952 0.047 a p < 0.05; bp < 0.01; cp < 0.001; Δχ2/2 refers to the chi-square change by constraining both intercept effect and slope effect equal across two gender groups. Table 4 Results of extended LGC modelling Temporal stability Intercept effect Slope effect Model fit Unstd. SE Std. Unstd. SE Std. Unstd. SE Std. Δχ2/2 CFI TLI RMSEA Hedonic well-being  Life satisfaction 0.388c 0.030 0.406 0.345c 0.030 0.340 3.488c 0.148 0.678 21.245c 0.961 0.933 0.062  Hopelessness 0.238c 0.022 0.260 –0.484c 0.025 –0.464 –3.182c 0.144 –0.592 5.965 0.971 0.950 0.051 Risk behaviour  Delinquent behaviour 0.489c 0.026 0.408 –0.031a 0.013 –0.057 –0.245b 0.079 –0.086 0.16 0.972 0.952 0.047  Problematic internet use 0.223c 0.020 0.243 –0.023c 0.005 –0.107 –0.240c 0.032 –0.215 1.779 0.971 0.950 0.047  Self-harm behaviour 0.084c 0.019 0.105 –0.009c 0.002 –0.114 –0.085c 0.012 –0.214 1.579 0.970 0.949 0.048  Suicidal behaviour 0.109c 0.017 0.143 –0.036c 0.004 –0.240 –0.289c 0.023 –0.367 1.971 0.972 0.952 0.047 Temporal stability Intercept effect Slope effect Model fit Unstd. SE Std. Unstd. SE Std. Unstd. SE Std. Δχ2/2 CFI TLI RMSEA Hedonic well-being  Life satisfaction 0.388c 0.030 0.406 0.345c 0.030 0.340 3.488c 0.148 0.678 21.245c 0.961 0.933 0.062  Hopelessness 0.238c 0.022 0.260 –0.484c 0.025 –0.464 –3.182c 0.144 –0.592 5.965 0.971 0.950 0.051 Risk behaviour  Delinquent behaviour 0.489c 0.026 0.408 –0.031a 0.013 –0.057 –0.245b 0.079 –0.086 0.16 0.972 0.952 0.047  Problematic internet use 0.223c 0.020 0.243 –0.023c 0.005 –0.107 –0.240c 0.032 –0.215 1.779 0.971 0.950 0.047  Self-harm behaviour 0.084c 0.019 0.105 –0.009c 0.002 –0.114 –0.085c 0.012 –0.214 1.579 0.970 0.949 0.048  Suicidal behaviour 0.109c 0.017 0.143 –0.036c 0.004 –0.240 –0.289c 0.023 –0.367 1.971 0.972 0.952 0.047 a p < 0.05; bp < 0.01; cp < 0.001; Δχ2/2 refers to the chi-square change by constraining both intercept effect and slope effect equal across two gender groups. Regarding risk behaviour, the initial levels of MIL predicted reduced delinquent behaviour, problematic internet use, self-harm behaviour and suicidal behaviour at Grade 12, adjusting their initial levels, respectively. Additionally, change in MIL negatively predicted these risk behaviours at Grade 12, adjusting their initial levels. In other words, the smaller the decrease in MIL over time, the lower the level of risk behaviour engagement at Grade 12. All models fitted the data well. Multi-group analyses found no gender differences in the intercept effects and slope effects except the life satisfaction model (Δχ2 (1) = 22.405, p < 0.001). Though the slope effect of MIL on life satisfaction was significant across male and female groups, the magnitude was stronger in the females (B = 3.380, SE = 0.316, β = 0.675, p < 0.001) relative to the males (B = 1.958, SE = 0.165, β = 0.472, p < 0.001). In other words, the decrease in MIL over time had a stronger impact on female adolescents’ life satisfaction relative to male adolescents. Besides studying the outcomes separately, we conducted supplementary analyses using one model examining the latent factor of hedonic well-being with life satisfaction and hopelessness as observed variables and the other model examining the latent factor of risk behaviour with the four risk behaviours as observed variables. The results remained largely the same. In the model of hedonic well-being (CFI = 0.975, TLI = 0.953, RMSEA = 0.047), the initial level of MIL (B = 0.581, SE = 0.056, β = 0.953, p < 0.001) and the change in MIL (B = 2.147, SE = 0.105, β = 0.799, p < 0.001) positively predicted well-being at Grade 12, adjusting its initial level (β = –0.021, SE = 0.078, p > 0.05). In the model of risk behaviour (CFI = 0.953, TLI = 0.927, RMSEA = 0.042), the initial level of MIL (B = –0.035, SE = 0.012, β = –0.116, p < 0.001) and change in MIL (B = –0.454, SE = 0.061, β = –0.325, p < 0.001) negatively predicted risk behaviour, adjusting its initial level (B = 0.462, SE = 0.057, β = 0.401, p < 0.001). Taken together, the level of MIL at Grade 7 and the change in MIL over time both contributed to the hedonic well-being and risk behaviour at Grade 12. Discussion The MIL and how this construct contributes to adolescent well-being have gained surging interest in the field of social work and other fields of youth research and service (Cheon and Canda, 2010; Holloway and Moss, 2010; Shek, 2012). However, the use of meaning-related practices in adolescents in social work is sparse (see Benavides, 2017). One of the reasons is the scarcity of potent empirical evidence showing the benefits of MIL for adolescents’ holistic development. Therefore, this pioneer study addressed an important question regarding whether MIL fosters subsequent adolescent well-being using six waves of data. As expected, we found that adolescents’ MIL at Grade 7 predicted increased hedonic well-being and reduced risk behaviour at Grade 12. Furthermore, we found that MIL decreased from Grades 7 to 11, and adolescents who experienced a faster decrease in MIL over time had poorer hedonic well-being and more risk behaviour at their graduation year. Most of these relationships did not vary across gender, suggesting that the positive effects of MIL are true for both male and female adolescents. By using a stringent longitudinal design with time-order effect, our study provides strong evidence for the predictive role of MIL in well-being. It affirms and extends the theoretical understanding on the role of MIL in fostering adolescent well-being, and further consolidates the groundwork for integrating MIL into social work practices, particularly youth practices (Cheon and Canda, 2010; George and Ellison, 2015). Theoretically, these findings provide support for and advance Frankl’s (1959) existential theory and the strength-based approach that regards spirituality as a developmental asset for fostering positive youth development (Benson et al., 2003; Shek and Wu, 2013) and the thesis that regards spirituality as a recovery resource for clients suffering from mental problems (Starnino et al., 2014) in two aspects. First, the presence of longitudinal effects of MIL suggests that MIL is predictive of hedonic well-being and risk behaviours and the effects may last over time. An initial high level of MIL may set a stage for the subsequent growth of well-being. As suggested by Brassai et al. (2011, p. 45), ‘the individuals feel greater presence of meaning when they understand themselves (e.g. self-acceptance), the world around them (e.g. environmental mastery), and their fit within the world (e.g. positive relationships)’. This is a developmental asset fostering one’s positive outlook towards current and future life. Additionally, individuals who have a higher level of MIL presumably have greater capacity to derive meanings from life experiences and better internal resource to cope with mental problems (Fry, 1998; Steger, 2012). Such spiritual strength may also serve as a resiliency factor that protects adolescents from engaging in risk behaviour when they encounter challenges and adversities (Brassai et al., 2011; Kleiman and Beaver, 2013). Particularly, this study provides initial evidence for the long-term protection for adolescent risk behaviours, including delinquent behaviour, problematic internet use, suicidal behaviour and self-harm behaviour, which fills the void between the decades of theoretical argument for the important effect of MIL in diminishing risk behaviour and the empirical support. The heightened hedonic well-being and lessened risk behaviour may, in turn, sustain MIL, which forms a virtuous circle. Therefore, the positive effect of MIL would be carried over from early adolescence to late adolescence. Our findings support the argument for the enduring effect of MIL (Baumeister et al., 2013). Life meaning that integrates the past and future is more likely to produce long-term effects on constructing a better life. Altogether, our study makes a significant contribution to enriching social workers’ understanding of youth well-being and risk behaviour by providing an additional perspective based on the existential approach. It is noteworthy that MIL declines over high-school years, and this developmental change has a psychological impact on adolescent well-being beyond that of the MIL level. Adolescents who experience a faster drop in life meaning over time would suffer greater cost to their hedonic well-being and behavioural adjustment. The greater deterioration in adolescent spiritual strength possibly reflects increasing existential challenges (e.g. difficulty in identifying meaningful life goals) yet insufficient support in adolescents’ lives. The accumulation of existential frustration over time probably leads to undesirable developmental outcomes. The current findings indicate a need to expand the scope of MIL theories by considering its dynamic nature and to provide more empirical evidence that explicates the developmental process of MIL over the lifespan. These insights also bring important practical implications for social workers. First, our findings reinforce the call for integrating spirituality into social work practice (Holloway and Moss, 2010; George and Ellison, 2015; Oxhandler and Pargament, 2014). When people encounter life challenges that possibly render them physically or psychological suffering, spirituality can be used to heal the pain (Starnino et al., 2014; Benavides, 2017). Indeed, MIL can be changed, as evidenced by youth programmes that help adolescents identify their life meaning (Ma and Shek, 2017) and meaning therapy that guides clients to establish enduring meaning and coax values from their life events and circumstances (Vos et al., 2015). As asserted by the theorist and practitioner of meaning therapy Paul Wong (1999), ‘human beings are capable of change and personal growth through learning’ (p. 48). Additionally, a meta-analysis of six meaning therapies on adult patients with serious diseases has demonstrated strong evidence in its effects on enhancing self-efficacy and reducing psychopathology (Vos et al., 2015). However, the application of spirituality-oriented practices still leaves behind adolescent clients (Cheon and Canda, 2010). It is still debatable whether it applies to adolescents who might be too young to wrestle with issues transcending beyond self and mundane lives (see Fitzgerald, 2005; Benavides, 2017). Our findings together with increasing effectiveness evidence of meaning practices (Vos et al., 2015) provide a suggestion to social workers and youth workers that it is worth integrating adolescent clients’ spirituality into the practice when dealing with their mental health. Helping adolescents coax meanings from their life experiences may also be effective to boost their hedonic well-being and reduce their risk behaviour. Furthermore, our findings highlight the need to address adolescent clients’ change of subjective experience in MIL. Adolescents who have experienced a rapid drop in the feeling of meaningfulness might also be at risk. Therefore, social work professionals and youth workers may need to consider such ‘change’ in their clients. Finally, considering social workers generally lack adequate training in spirituality (Holloway, 2007), we call for integrating spiritual constructs and spirituality-based practice into social work curricula. Social work institutes should provide front line social workers with more opportunities to receive related training and supervision. Despite its pioneer nature, there are four limitations of the study. First, this study only focused on perceived life meaning. Besides that, other related dimensions such as sense of congruence (Steger, 2012) are worth studying. Second, the measurement may limit the understanding of the outcomes. We relied on self-report for all the measures, because MIL and hedonic well-being tap into subjective feelings and experiences, while risk behaviours of deliberate self-harm and suicide are private. Yet, it is still desirable to involve reports of significant others and use objective measures, as it would give a more holistic picture about the inquiry. Additionally, the reliability of suicidal behaviour was not high, probably because of the discrepancy in the occurrence rate between suicidal ideation (Grade 7: 13.7 per cent) and suicidal plan (Grade 7: 4.9 per cent)/attempt (Grade 7: 4.7 per cent). We recommend future studies to examine suicidal ideation and suicidal behaviour separately with detailed scales. Third, the current sample was limited to Hong Kong Chinese adolescents. According to the small body of evidence derived from mainland Chinese youngsters (Wang et al., 2016; Zhang et al., 2015) showing that MIL was positively associated with hedonic well-being and negatively associated with risk behaviour, it is highly likely that the current findings can be generated into other Chinese adolescents. However, further studies with Chinese and non-Chinese samples are needed to replicate our findings. Lastly, the longitudinal design cannot fully demonstrate the causal relationship between life meaning and adolescent developmental outcomes in the intervention contexts, and thus further studies are needed to investigate practice-related factors in the intervention contexts. Nevertheless, the present study generated pioneer findings regarding the role of change in life meaning in adolescent well-being, which has practical social work implications. Funding The Project P.A.T.H.S. and this paper are financially supported by the Hong Kong Jockey Club Charities Trust. 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( 2015 ) ‘ The relationship between impulsivity and Internet addiction in Chinese college students: A moderated mediation analysis of meaning in life and self-esteem ’, Plos One , 10 ( 7 ), doi:10.1371/journal.pone.0131597. © The Author(s) 2018. Published by Oxford University Press on behalf of The British Association of Social Workers. All rights reserved This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The British Journal of Social Work Oxford University Press

The Influence of Meaning in Life on Adolescents’ Hedonic Well-Being and Risk Behaviour: Implications for Social Work

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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press on behalf of The British Association of Social Workers. All rights reserved
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0045-3102
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1468-263X
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10.1093/bjsw/bcy029
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Abstract

Abstract The strength-based approach argues that promoting adolescents’ developmental assets would contribute to their well-being. This study investigated meaning in life (MIL) as one of such developmental assets. Based on the annual assessment of 3,328 Hong Kong Chinese adolescents from Grade 7 to Grade 12, we found that MIL at Grade 7 predicted heightened hedonic well-being and reduced risk behaviour at Grade 12; slower decrease in MIL from Grade 7 to Grade 11 predicted heightened hedonic well-being and reduced risk behaviour at Grade 12 with the initial level of outcomes controlled. Findings highlight the importance of MIL as a developmental asset fostering adolescent well-being. Implications of the findings for advancing theory of MIL and social work practice are discussed. Meaning in life, hedonic well-being, risk behaviour, Chinese adolescents, longitudinal study Introduction Adolescents’ well-being is one of the primary concerns of social work and youth-helping professionals. Well-being refers to ‘the state of being happy, healthy, and prosperous’ (Black and Jeffery, 2007, p. 1084) and includes both the positive (e.g. life satisfaction) and negative indicators (e.g. behavioural problems; Lee, 2014). Teenagers face myriad transformations in multiple areas of their lives on their path to adulthood, including self, family, peer and school, which increase the challenges for sustaining well-being (Call et al., 2002). Research has shown that a significant proportion of teenagers experience stress and depression in their daily lives (APA, 2014) and display risk behaviour such as suicide attempts and non-suicidal self-harm (Kokkevi et al., 2012), as well as problematic internet use (Tsitsika et al., 2014). In Hong Kong, the report of the Hong Kong Mental Health Index Research Group (2014) also showed that the well-being index of young people declined to a level that was close to the level indicative of clinical depression. The strength-based approach (Lerner, 2004; Starnino et al., 2014) posits that promoting adolescents’ meaning in life (MIL) is one of the potential solutions to the vulnerability to dampened well-being in adolescence, because MIL is one of the spiritual strengths that help improve positive functioning and reduce psychological problems. However, the existing research on the role of MIL in the adolescent period is still inadequate, which hinders the theoretical advancement and practical improvement in promoting adolescent well-being. In the field of social work, there is a growing awareness of integrating clients’ spiritual strengths into social work theory and practice (Cheon and Canda, 2010; Starnino et al., 2014; George and Ellison, 2015). Spirituality refers to the search for connectedness, meaning, purpose and contribution from life experiences, and MIL is one of the essential components (Benson et al., 2003; Cheon and Canda, 2010). Spirituality can serve as an internal resource that facilitates the recovery of clients with mental problems (Starnino et al., 2014). As argued by Gardner (2017), ‘spirituality is part of each person’s experience and therefore needs to be seen as an integral part of practice’ (p. 305). Yet, such concern is largely limited to adult targets with very few social work studies examining the spiritual concerns of young people (Cheon and Canda, 2010). It is still debatable whether spirituality-related practices apply to adolescents, because some people regard them as ‘too young’ to think about and make good use of spirituality (see Fitzgerald, 2005). In the strength-based approach, spirituality has been regarded as a developmental asset that nurtures positive youth development (Benson et al., 2003). Cheon and Canda (2010) called for integrating spirituality into social work practice and other youth-helping activities with reference to the strength-based approach. They argued that ‘it is critical for social workers to increase their awareness of their young clients’ spirituality and whether it is relevant to practice goals in child welfare, school social work, family services, and other youth-serving contexts’ (p. 123). Unfortunately, related empirical evidence that examines adolescents’ spiritual development such as the developmental change in MIL is sparse. To advance the theoretical formulation of spiritual development and spirituality-related practices in youth, we need more empirical evidence showing the benefits of promoting MIL in adolescents. Therefore, this study aimed at understanding the role of MIL in multiple markers of adolescent well-being with a six-wave longitudinal design. The findings of the current study would have implications for advancing theoretical understanding on the role of MIL in youth development as well as innovation of social work practice addressing adolescent well-being. MIL and adolescent well-being MIL was defined as ‘ontological significance of life from the view of the experiencing individual’ (Crumbaugh and Maholick, 1964, p. 185). People who have this subjective judgement and experience that ‘life is meaningful’ are able to make sense of the life events, see the significance of their lives and establish an enduring purpose that directs their lives (Steger et al., 2009). In youth development, the strength-based approach regards MIL as a developmental asset, which refers to positive experiences and qualities that direct adolescents away from risk behaviour, foster their resilience and promote their thriving (Benson et al., 2003; Benson and Scales, 2011). MIL may foster a happy life. MIL is regarded as an indicator of eudaimonic well-being that entails fulfilment of human potentials and personal growth (Ryff, 1989), which is different from hedonic well-being that refers to the presence of happiness and contentment (Baumeister et al., 2013). A meaningful life is not equal to a happy life, but possible to foster a happy life. If one interprets the events in his/her life in a meaningful and coherent way, he/she understands that his/her life holds value, which is presumably related to positive outlook towards life (Steger, 2012). Several studies found that adolescents who reported greater MIL had higher life satisfaction (Ho et al., 2010), happiness (Kiang and Fuligni, 2010) and sense of mastery (Shek, 2001), as well as lower anxiety (Shek, 1992), depression (Ho et al., 2010) and hopelessness (Brassai et al., 2012). On top of that, MIL may protect adolescents from risk behaviour (Brassai et al., 2011; Zhang et al., 2015). According to Frankl’s (1959) theory, questing for MIL is a fundamental human motive, which was called ‘will to meaning’. People who fail to fulfil the MIL may experience ‘existential vacuum’ (existential frustration) and consequently psychological problems would occur to fill this vacuum. The lack of life meaning is manifested in a feeling of boredom and apathy (Maddi, 1967; Fahlman et al., 2009). Boredom as a loss of interest in life may render one to engage in activities that are sensuously exciting, such as breaking the rules or norms (Wegner and Flisher, 2009) or indulging oneself in the virtual world (Lin et al., 2009). Apathy as a loss of motivation to take action is a symptom of depression, which can also trigger risk behaviour such as suicidal attempt or non-suicidal self-harm (Kandel et al., 1991; Jacobson and Gould, 2007). Frankl’s theory has been supported by a handful of studies. For example, Brassai et al. (2011)’s study found that MIL was negatively related to binge drinking, unsafe sex and lack of exercises and diet control among female adolescents, and to illicit drug and sedative use among male adolescents. Despite the growing evidence showing positive psychological correlates of MIL in adolescents, the argument that MIL is the foundation of a better life (Frankl, 1959; Steger, 2012) remains a conjecture only. Specifically, can MIL serve as a foundation that produces happiness and keeps adolescents away from risk behaviour? To answer this question, we need evidence supporting that MIL is predictive of hedonic well-being and risk behaviour, and the effects are long-lasting. Yet, the literature largely based on the concurrent associations between MIL and other well-being indicators (e.g. Steger et al., 2009; Kiang and Witkow, 2015) provides meagre evidence for this question. The cross-sectional design, even panel data without including time-order effects (Kiang and Witkow, 2015), informs little about the directionality of association (e.g. can MIL predict hedonic well-being?) and fails to confirm whether the meaningful experience simply elicits instant good feelings or produces enduring benefits (i.e. would MIL have a long-term effect on hedonic well-being?). Theoretically, adolescents’ spiritual development is a powerful resource that nurtures positive youth development (Benson et al., 2003). Additionally, Baumeister et al. (2013) argued that meaning is not subject to the present moment, but goes beyond the here and now, and thus it may have a more enduring effect. Empirically, to validate these arguments, a longitudinal design that tests the time-order effect of MIL on succeeding outcomes is highly needed. So far, there is only one direct piece of evidence (Shek, 1998), which found that MIL predicted decreased psychological disturbance one year later among adolescents. To fill the gap between the theoretical argument for the importance of MIL in human welfare (particularly adolescent welfare) and the potent empirical support, we certainly need more longitudinal studies that extend the time frame and include other well-being indicators. Additionally, existing theories of MIL seldom address the dynamic nature of MIL. Much of the research assumed MIL as a relatively stable and trait-like attribute about the degree to which one perceives his/her life to be meaningful (e.g. Ho et al., 2010). However, during secondary-school years, adolescents’ MIL dropped despite a minor rebound in Grade 11 (Shek and Lin, 2017). In addition to the simple level at one time point, many studies have found that how a developmental attribute changes over time has additional psychological implications (e.g. Wang and Pomerantz, 2009). While lacking a sense of meaning is harmful, experiencing a decline in MIL across time may bring about an additional detrimental effect on adolescent well-being. The forefront of contemporary theories about youth development and social work practice concerns the ‘change’ in persons, with an objective to help individuals to change in a direction that fosters individual well-being and social goods (Benson et al., 2006). However, to our best knowledge, the empirical evidence about the change in MIL and its consequences is almost missing. Investigating the systematic change in MIL expands the scope of theoretical understanding on the role of MIL in well-being, which serves as an active response to the contemporary theory advancement. Altogether, to fill the gap regarding the theoretical understanding of the dynamic nature of MIL, we need studies that trace the change in MIL over time instead of focusing on the static level of MIL. Current study With regard to the aforementioned research gaps, the current study primarily aims at understanding how MIL influences different aspects of adolescent well-being using a developmental perspective. With a longitudinal design of six years of annual assessments, we address four research questions. The first two questions test whether the initial level of MIL and the change in MIL would predict adolescents’ hedonic well-being indexed by life satisfaction and hopelessness. There is evidence suggesting that MIL is positively related to life satisfaction (e.g. Ho et al., 2010) and negatively related to hopelessness among adolescents (Brassai et al., 2012) and young adults (Shek, 1993). Therefore, we hypothesised that MIL at Grade 7 would predict increased life satisfaction and decreased hopelessness at Grade 12 (Hypotheses 1a and 1b). Moreover, according to the literature reviewed above, it is reasonable to believe that a declining trend of MIL indicates poor hedonic well-being in adolescents. Therefore, we hypothesised that the faster rate of decline in MIL from Grades 7 to 11 would predict decreased life satisfaction and increased hopelessness at Grade 12 (Hypotheses 2a and 2b). The last two questions regard whether the initial level of MIL and the change in MIL would predict risk behaviour among adolescents. We examined adolescents’ risk behaviour indexed by delinquent behaviour, problematic internet use, non-suicidal self-harm behaviour and suicidal behaviour. The most obvious connection is the negative relationship between MIL and suicide, since they look incompatible (Kleiman and Beaver, 2013). Previous research on adults has established the link between MIL and suicide (Kleiman and Beaver, 2013). Furthermore, one previous piece of research revealed the inverse relationship between MIL and delinquent behaviour among adolescents (Shek et al., 1994). The presence of life meaning was also inversely related to self-harm behaviour (Kress et al., 2015) and internet addiction (Zhang et al., 2015) among young adults. We thus expected that MIL at Grade 7 would predict decreased delinquent behaviour, problematic internet use, non-suicidal self-harm behaviour and suicidal behaviour at Grade 12 (Hypotheses 3a–3d). Similarly, we also expected that the faster rate of decline in MIL from Grades 7 to 11 would predict increased risk behaviours at Grade 12 (Hypotheses 4a–4d). We also explored the potential role of gender in the developmental change in MIL and in the effect of MIL. If questing for meaning is a fundamental human motive, the presence of MIL should be equally important for females and males, which is evident in some previous studies (e.g. Shek, 1998) but not the others (e.g. Brassai et al., 2011). Due to the inconsistent results, we explored whether the change in MIL and its effects would vary as a function of gender. Method Participants and procedure Participants were from the Project P.A.T.H.S. (i.e. Positive Adolescent Training through Holistic Social Programmes), a six-year longitudinal study in Hong Kong. Project P.A.T.H.S. consisted of annual assessments over six years among high-school students from Grade 7 to Grade 12 (Shek and Lin, 2017). At Grade 7, a total of 3,328 students joined the project (Mean age = 12.59 ± 0.74 years; 51.7 per cent male). The attrition rates ranged from 12.7 per cent to 28.3 per cent due to students’ drop-out, transferring schools or absence on the day of assessment. The interval between adjacent waves was about one year except for that between Wave 5 and Wave 6 (i.e. ten months), as the students needed to sit the public entrance examination for college. Table 1 shows the detailed characteristics of the participants. Table 1 Descriptive information about the participants and variable of MIL Grade 7 % Grade 8 % Grade 9 % Grade 10 % Grade 11 % Grade 12 % N (participants) 3,328 2,905 2,860 2,684 2,474 2,385 Average age 12.59 13.59 14.54 15.50 16.37 17.20 Gender Male 1,719 51.7 1,445 49.7 1,424 49.8 1,323 49.3 1,187 48.0 1,144 48.0 Female 1,572 47.2 1,429 49.2 1,406 49.2 1,330 49.6 1,258 50.8 1,211 50.8 Economic disadvantage  NOT receiving CSSA 2,606 78.3 2,309 79.5 2,290 80.1 2,144 79.9 1,985 80.2 1,913 80.2  Receiving CSSA 225 6.8 191 6.6 184 6.4 178 6.6 159 6.4 157 6.6 Family intactness  Intact families 2,781 83.6 2,446 84.2 2,418 84.5 2,283 85.1 2,097 84.8 2,029 85.1  Non-intact families 515 15.5 432 14.9 418 14.6 379 14.1 355 14.3 335 14.0 Meaning in life Mean 5.14 5.01 5.01 4.96 4.92 SD 1.32 1.29 1.26 1.23 1.24 α 0.88 0.89 0.90 0.92 0.91 Inter-item# 0.71 0.74 0.76 0.78 0.78 Grade 7 % Grade 8 % Grade 9 % Grade 10 % Grade 11 % Grade 12 % N (participants) 3,328 2,905 2,860 2,684 2,474 2,385 Average age 12.59 13.59 14.54 15.50 16.37 17.20 Gender Male 1,719 51.7 1,445 49.7 1,424 49.8 1,323 49.3 1,187 48.0 1,144 48.0 Female 1,572 47.2 1,429 49.2 1,406 49.2 1,330 49.6 1,258 50.8 1,211 50.8 Economic disadvantage  NOT receiving CSSA 2,606 78.3 2,309 79.5 2,290 80.1 2,144 79.9 1,985 80.2 1,913 80.2  Receiving CSSA 225 6.8 191 6.6 184 6.4 178 6.6 159 6.4 157 6.6 Family intactness  Intact families 2,781 83.6 2,446 84.2 2,418 84.5 2,283 85.1 2,097 84.8 2,029 85.1  Non-intact families 515 15.5 432 14.9 418 14.6 379 14.1 355 14.3 335 14.0 Meaning in life Mean 5.14 5.01 5.01 4.96 4.92 SD 1.32 1.29 1.26 1.23 1.24 α 0.88 0.89 0.90 0.92 0.91 Inter-item# 0.71 0.74 0.76 0.78 0.78 Participants who did not provide demographic information were not included. #Mean of inter-item correlations. Table 1 Descriptive information about the participants and variable of MIL Grade 7 % Grade 8 % Grade 9 % Grade 10 % Grade 11 % Grade 12 % N (participants) 3,328 2,905 2,860 2,684 2,474 2,385 Average age 12.59 13.59 14.54 15.50 16.37 17.20 Gender Male 1,719 51.7 1,445 49.7 1,424 49.8 1,323 49.3 1,187 48.0 1,144 48.0 Female 1,572 47.2 1,429 49.2 1,406 49.2 1,330 49.6 1,258 50.8 1,211 50.8 Economic disadvantage  NOT receiving CSSA 2,606 78.3 2,309 79.5 2,290 80.1 2,144 79.9 1,985 80.2 1,913 80.2  Receiving CSSA 225 6.8 191 6.6 184 6.4 178 6.6 159 6.4 157 6.6 Family intactness  Intact families 2,781 83.6 2,446 84.2 2,418 84.5 2,283 85.1 2,097 84.8 2,029 85.1  Non-intact families 515 15.5 432 14.9 418 14.6 379 14.1 355 14.3 335 14.0 Meaning in life Mean 5.14 5.01 5.01 4.96 4.92 SD 1.32 1.29 1.26 1.23 1.24 α 0.88 0.89 0.90 0.92 0.91 Inter-item# 0.71 0.74 0.76 0.78 0.78 Grade 7 % Grade 8 % Grade 9 % Grade 10 % Grade 11 % Grade 12 % N (participants) 3,328 2,905 2,860 2,684 2,474 2,385 Average age 12.59 13.59 14.54 15.50 16.37 17.20 Gender Male 1,719 51.7 1,445 49.7 1,424 49.8 1,323 49.3 1,187 48.0 1,144 48.0 Female 1,572 47.2 1,429 49.2 1,406 49.2 1,330 49.6 1,258 50.8 1,211 50.8 Economic disadvantage  NOT receiving CSSA 2,606 78.3 2,309 79.5 2,290 80.1 2,144 79.9 1,985 80.2 1,913 80.2  Receiving CSSA 225 6.8 191 6.6 184 6.4 178 6.6 159 6.4 157 6.6 Family intactness  Intact families 2,781 83.6 2,446 84.2 2,418 84.5 2,283 85.1 2,097 84.8 2,029 85.1  Non-intact families 515 15.5 432 14.9 418 14.6 379 14.1 355 14.3 335 14.0 Meaning in life Mean 5.14 5.01 5.01 4.96 4.92 SD 1.32 1.29 1.26 1.23 1.24 α 0.88 0.89 0.90 0.92 0.91 Inter-item# 0.71 0.74 0.76 0.78 0.78 Participants who did not provide demographic information were not included. #Mean of inter-item correlations. Participants completed a battery of questionnaires in a classroom setting during school hours with the presence of a trained research assistant. This study has obtained ethical approval from the Human Subjects Ethics Sub-committee of The Hong Kong Polytechnic University. Parental consent and school consent were obtained before the administration of the first wave of assessment and students’ consent was obtained at the beginning of every wave of assessment. Instruments Participants reported on MIL, hedonic well-being and risk behaviour using self-administrated questionnaires. The scales used have all been successfully adopted in previous studies on Chinese adolescents (e.g. Shek, 2004) and showed acceptable internal consistencies in this study. Tables 1 and 2 show the descriptive information about the study variables. Table 2 Descriptive information of the outcome variables Grade 7 Grade 12 Grade 7 to Grade 12 Variable Mean SD α Inter- item# Mean SD α Inter- item# r Life satisfaction 3.95 1.11 0.85 0.55 3.60 1.05 0.88 0.62 0.372 Hopelessness 2.68 1.18 0.85 0.55 2.66 1.07 0.89 0.63 0.330 Delinquent behaviour 0.39 0.47 0.70 0.25 0.49 0.55 0.69 0.29 0.348 Problematic internet use 0.23 0.24 0.79 0.28 0.17 0.22 0.81 0.32 0.227 Deliberate self-harm behaviour 0.04 0.10 0.83 0.25 0.02 0.08 0.86 0.40 0.083 Suicidal behaviour 0.08 0.21 0.68 0.44 0.05 0.16 0.61 0.40 0.137 Grade 7 Grade 12 Grade 7 to Grade 12 Variable Mean SD α Inter- item# Mean SD α Inter- item# r Life satisfaction 3.95 1.11 0.85 0.55 3.60 1.05 0.88 0.62 0.372 Hopelessness 2.68 1.18 0.85 0.55 2.66 1.07 0.89 0.63 0.330 Delinquent behaviour 0.39 0.47 0.70 0.25 0.49 0.55 0.69 0.29 0.348 Problematic internet use 0.23 0.24 0.79 0.28 0.17 0.22 0.81 0.32 0.227 Deliberate self-harm behaviour 0.04 0.10 0.83 0.25 0.02 0.08 0.86 0.40 0.083 Suicidal behaviour 0.08 0.21 0.68 0.44 0.05 0.16 0.61 0.40 0.137 All the correlations (r) were significant. #Mean of inter-item correlations. Table 2 Descriptive information of the outcome variables Grade 7 Grade 12 Grade 7 to Grade 12 Variable Mean SD α Inter- item# Mean SD α Inter- item# r Life satisfaction 3.95 1.11 0.85 0.55 3.60 1.05 0.88 0.62 0.372 Hopelessness 2.68 1.18 0.85 0.55 2.66 1.07 0.89 0.63 0.330 Delinquent behaviour 0.39 0.47 0.70 0.25 0.49 0.55 0.69 0.29 0.348 Problematic internet use 0.23 0.24 0.79 0.28 0.17 0.22 0.81 0.32 0.227 Deliberate self-harm behaviour 0.04 0.10 0.83 0.25 0.02 0.08 0.86 0.40 0.083 Suicidal behaviour 0.08 0.21 0.68 0.44 0.05 0.16 0.61 0.40 0.137 Grade 7 Grade 12 Grade 7 to Grade 12 Variable Mean SD α Inter- item# Mean SD α Inter- item# r Life satisfaction 3.95 1.11 0.85 0.55 3.60 1.05 0.88 0.62 0.372 Hopelessness 2.68 1.18 0.85 0.55 2.66 1.07 0.89 0.63 0.330 Delinquent behaviour 0.39 0.47 0.70 0.25 0.49 0.55 0.69 0.29 0.348 Problematic internet use 0.23 0.24 0.79 0.28 0.17 0.22 0.81 0.32 0.227 Deliberate self-harm behaviour 0.04 0.10 0.83 0.25 0.02 0.08 0.86 0.40 0.083 Suicidal behaviour 0.08 0.21 0.68 0.44 0.05 0.16 0.61 0.40 0.137 All the correlations (r) were significant. #Mean of inter-item correlations. MIL Participants reported their feelings that life is meaningful via the three-item subscale of spirituality on a seven-point Likert scale (e.g. life is empty or exciting). This scale is derived from subscale of spirituality of the short-version Chinese Positive Youth Development Scale (CPYDS, Shek et al., 2007). This subscale was originally adapted from the Purpose in Life Questionnaire (Crumbaugh and Maholick, 1964; Shek et al., 1987), which has been successfully used in Chinese adolescent samples (Shek, 1992, 2001). Well-being The five-item translated version of the Satisfaction with Life Scale (Diener et al., 1985; Shek, 2004) was used to measure participants’ overall evaluation of their life satisfaction using a six-point Likert scale (e.g. one is satisfied with life). Adapted from the Hopelessness Scale (Beck et al., 1974), the five-item Chinese Hopelessness Scale (Shek, 1993) was used to measure participants’ sense of hopelessness using a six-point Likert scale (e.g. future seems gloomy). Risk behaviour Delinquent behaviour: Participants reported their frequency of engagement in twelve delinquent acts in the past year, including cheating, stealing, running away from home, truancy, damaging others’ properties, speaking foul language, assault, gang fighting, having sexual intercourse with others, trespassing behaviour on a seven-point scale (0 = never, 6 = more than ten times; Shek, 2004). Problematic internet use: Young’s ten-item Internet Addiction Test (IAT) (Young, 1998), which has been validated in the Chinese context (Shek et al., 2008), was adopted to measure participants’ problematic internet use. Participants answered yes or no to ten addictive symptoms (e.g. staying longer online than one intended) in the past year. We used mean scores across ten items to indicate the degree of the problematic internet use. Deliberate self-harm: A checklist of seventeen forms of deliberate self-harm behaviour without suicidal ideation was used to measure participants’ occurrence of deliberate self-harm behaviour (Shek and Yu, 2012). The participants reported whether they had engaged in these behaviours in the past year, such as wrist cutting, self-scratching, carving words on the body (1= yes, 0 = no). Suicidal behaviour: A three-item checklist was used to measure participants’ suicidal ideation, suicidal plan and suicidal attempt in the past year, respectively (1 = yes, 0 = no) (Shek and Yu, 2012). Data-analysis plan The current study primarily tested whether MIL and its change would predict adolescent well-being over time. Latent growth curve (LGC) modelling was used via AMOS 22.0 to address the questions with full information maximum likelihood to address the incomplete data. LGC modelling allows researchers to capture the intra-individual change of psychological attribute over time and its relations to other variables (Duncan et al., 1999). Therefore, it allows us to test how the change in MIL affects adolescent well-being. First, we used LGC to establish the developmental trajectory of MIL across five years. Next, we used extended LGC modelling to test the associations between MIL and the indicators of adolescent well-being. As the effects might vary across different indicators, each model estimated one outcome variable. As shown in Figure 1, each model consisted of two latent factors that could be correlated. By specifying the factor loadings from the ‘intercept’ to the MIL variables assessed at the five waves as 1, the latent factor of intercept indicates the average level of MIL at the initial wave. By specifying the factor loadings from the ‘slope’ to the MIL variable assessed at the five waves as 0, 1, 2, 3 and 4, respectively, the latent factor of the slope indicates the rate of change in MIL across the five waves over high-school years. The effect of MIL at the initial wave (Grade 7) on the well-being or risk behaviour at Grade 12 was estimated as the ‘intercept effect’ and the effect of change in MIL (from Grades 7 to 11) on well-being or risk behaviour at Grade 12 was estimated as the ‘slope effect’. These effects were estimated with the temporal stability of the outcome variable controlled. Additionally, as previous studies have suggested that economic status and family intactness might affect the outcome variables (Shek and Lin, 2015, 2016), we included them as control variables. Finally, we explored gender differences in these models by comparing the model with intercept effect and slope effect constrained to be equal across the male and female groups with the unconstrained model (i.e. multi-group analysis). We used CFI, TLI (>0.90 acceptable fit; >0.95 good fit) and RMSEA (<0.08 acceptable fit; <0.05 good fit) for model fit (Byrne, 2001). Figure 1 View largeDownload slide A schematic illustration of the extended LGC model. MIL, meaning in life. Demographic variables were also controlled in this model. Error terms of observed variables are omitted in the figure. Figure 1 View largeDownload slide A schematic illustration of the extended LGC model. MIL, meaning in life. Demographic variables were also controlled in this model. Error terms of observed variables are omitted in the figure. Results Change in MIL Using LGC modelling, we found that MIL decreased over time (B = –0.105, SE = 0.019, p < 0.001), but the decrease rate slowed down (B = 0.011, SE = 0.004, p = 0.010) with gender, economic status and family intactness as control variables. In addition, females (B = –0.054, SE = 0.023, p = 0.019), non-poor ones (B = –0.046, SE = 0.013, p < 0.001) and those living in intact families (B = 0.097, SE = 0.017, p < 0.001) reported higher levels of MIL at the initial assessment than males, poor ones and those living in non-intact families. However, those from intact families experienced a slightly faster decline in MIL (B = –0.029, SE = 0.014, p = 0.031). The model had a good fit (CFI = 0.995, TLI = 0.986, RMSEA = 0.026). As the high-school period was dominated by the declining trend with a small rebound in Grade 11, we only included the linear change in the following extended LGC models. Intercept effects and slope effects Bivariate correlations provided information on the unadjusted associations between MIL and adolescent well-being. The correlations (see Table 3) indicated that the initial MIL was significantly associated with life satisfaction, hopelessness, delinquent behaviour, problematic internet use, deliberate self-harm behaviour and suicidal behaviour at Grade 7 and those at Grade 12. Additionally, the levels of MIL across the five waves were significantly associated with these outcome variables. Table 3 Bivariate correlations of outcome variables (Grade 12) and their predictors Life satisfaction Hopelessness Delinquent behaviour Problematic internet use Deliberate self-harm behaviour Suicidal behaviour Meaning in life Grade 7 0.276 –0.287 –0.100 –0.103 –0.054 –0.136 (0.604) (–0.459) (–0.263) (–0.281) (–0.243) (–0.291) Grade 8 0.322 –0.364 –0.106 –0.099 –0.059 –0.147 Grade 9 0.397 –0.381 –0.074 –0.107 –0.094 –0.161 Grade 10 0.441 –0.443 –0.116 –0.141 –0.099 –0.215 Grade 11 0.488 –0.443 –0.116 –0.141 –0.099 –0.215 Demographic variables (Grade 7) Gender (0 = female, 1 = male) –0.042 0.103 0.168 0.042 0.001 –0.011 Economic status (0 = non-poor, 1 = poor) –0.070 0.054 0.028 0.038 0.049 0.046 Family intactness (0 = non-intact family, 1 = intact family) 0.048 –0.041 –0.052 –0.010 –0.053 –0.075 Life satisfaction Hopelessness Delinquent behaviour Problematic internet use Deliberate self-harm behaviour Suicidal behaviour Meaning in life Grade 7 0.276 –0.287 –0.100 –0.103 –0.054 –0.136 (0.604) (–0.459) (–0.263) (–0.281) (–0.243) (–0.291) Grade 8 0.322 –0.364 –0.106 –0.099 –0.059 –0.147 Grade 9 0.397 –0.381 –0.074 –0.107 –0.094 –0.161 Grade 10 0.441 –0.443 –0.116 –0.141 –0.099 –0.215 Grade 11 0.488 –0.443 –0.116 –0.141 –0.099 –0.215 Demographic variables (Grade 7) Gender (0 = female, 1 = male) –0.042 0.103 0.168 0.042 0.001 –0.011 Economic status (0 = non-poor, 1 = poor) –0.070 0.054 0.028 0.038 0.049 0.046 Family intactness (0 = non-intact family, 1 = intact family) 0.048 –0.041 –0.052 –0.010 –0.053 –0.075 The numbers in the parentheses were the correlations between meaning in life and outcome variables at Grade 7; |r| larger than 0.041 was significant at p < 0.05. Table 3 Bivariate correlations of outcome variables (Grade 12) and their predictors Life satisfaction Hopelessness Delinquent behaviour Problematic internet use Deliberate self-harm behaviour Suicidal behaviour Meaning in life Grade 7 0.276 –0.287 –0.100 –0.103 –0.054 –0.136 (0.604) (–0.459) (–0.263) (–0.281) (–0.243) (–0.291) Grade 8 0.322 –0.364 –0.106 –0.099 –0.059 –0.147 Grade 9 0.397 –0.381 –0.074 –0.107 –0.094 –0.161 Grade 10 0.441 –0.443 –0.116 –0.141 –0.099 –0.215 Grade 11 0.488 –0.443 –0.116 –0.141 –0.099 –0.215 Demographic variables (Grade 7) Gender (0 = female, 1 = male) –0.042 0.103 0.168 0.042 0.001 –0.011 Economic status (0 = non-poor, 1 = poor) –0.070 0.054 0.028 0.038 0.049 0.046 Family intactness (0 = non-intact family, 1 = intact family) 0.048 –0.041 –0.052 –0.010 –0.053 –0.075 Life satisfaction Hopelessness Delinquent behaviour Problematic internet use Deliberate self-harm behaviour Suicidal behaviour Meaning in life Grade 7 0.276 –0.287 –0.100 –0.103 –0.054 –0.136 (0.604) (–0.459) (–0.263) (–0.281) (–0.243) (–0.291) Grade 8 0.322 –0.364 –0.106 –0.099 –0.059 –0.147 Grade 9 0.397 –0.381 –0.074 –0.107 –0.094 –0.161 Grade 10 0.441 –0.443 –0.116 –0.141 –0.099 –0.215 Grade 11 0.488 –0.443 –0.116 –0.141 –0.099 –0.215 Demographic variables (Grade 7) Gender (0 = female, 1 = male) –0.042 0.103 0.168 0.042 0.001 –0.011 Economic status (0 = non-poor, 1 = poor) –0.070 0.054 0.028 0.038 0.049 0.046 Family intactness (0 = non-intact family, 1 = intact family) 0.048 –0.041 –0.052 –0.010 –0.053 –0.075 The numbers in the parentheses were the correlations between meaning in life and outcome variables at Grade 7; |r| larger than 0.041 was significant at p < 0.05. Table 4 presents the results of extended LGC models based on the whole sample. Regarding hedonic well-being, the initial levels of MIL predicted heightened life satisfaction and lessened hopelessness at Grade 12, adjusting the initial levels of life satisfaction and hopelessness, respectively. Additionally, change in MIL positively predicted life satisfaction and negatively predicted hopelessness at Grade 12, adjusting their initial levels. In other words, the smaller the decrease in MIL over the first five school years, the higher the level of life satisfaction and the lower the level of hopelessness at the final year. Table 4 Results of extended LGC modelling Temporal stability Intercept effect Slope effect Model fit Unstd. SE Std. Unstd. SE Std. Unstd. SE Std. Δχ2/2 CFI TLI RMSEA Hedonic well-being  Life satisfaction 0.388c 0.030 0.406 0.345c 0.030 0.340 3.488c 0.148 0.678 21.245c 0.961 0.933 0.062  Hopelessness 0.238c 0.022 0.260 –0.484c 0.025 –0.464 –3.182c 0.144 –0.592 5.965 0.971 0.950 0.051 Risk behaviour  Delinquent behaviour 0.489c 0.026 0.408 –0.031a 0.013 –0.057 –0.245b 0.079 –0.086 0.16 0.972 0.952 0.047  Problematic internet use 0.223c 0.020 0.243 –0.023c 0.005 –0.107 –0.240c 0.032 –0.215 1.779 0.971 0.950 0.047  Self-harm behaviour 0.084c 0.019 0.105 –0.009c 0.002 –0.114 –0.085c 0.012 –0.214 1.579 0.970 0.949 0.048  Suicidal behaviour 0.109c 0.017 0.143 –0.036c 0.004 –0.240 –0.289c 0.023 –0.367 1.971 0.972 0.952 0.047 Temporal stability Intercept effect Slope effect Model fit Unstd. SE Std. Unstd. SE Std. Unstd. SE Std. Δχ2/2 CFI TLI RMSEA Hedonic well-being  Life satisfaction 0.388c 0.030 0.406 0.345c 0.030 0.340 3.488c 0.148 0.678 21.245c 0.961 0.933 0.062  Hopelessness 0.238c 0.022 0.260 –0.484c 0.025 –0.464 –3.182c 0.144 –0.592 5.965 0.971 0.950 0.051 Risk behaviour  Delinquent behaviour 0.489c 0.026 0.408 –0.031a 0.013 –0.057 –0.245b 0.079 –0.086 0.16 0.972 0.952 0.047  Problematic internet use 0.223c 0.020 0.243 –0.023c 0.005 –0.107 –0.240c 0.032 –0.215 1.779 0.971 0.950 0.047  Self-harm behaviour 0.084c 0.019 0.105 –0.009c 0.002 –0.114 –0.085c 0.012 –0.214 1.579 0.970 0.949 0.048  Suicidal behaviour 0.109c 0.017 0.143 –0.036c 0.004 –0.240 –0.289c 0.023 –0.367 1.971 0.972 0.952 0.047 a p < 0.05; bp < 0.01; cp < 0.001; Δχ2/2 refers to the chi-square change by constraining both intercept effect and slope effect equal across two gender groups. Table 4 Results of extended LGC modelling Temporal stability Intercept effect Slope effect Model fit Unstd. SE Std. Unstd. SE Std. Unstd. SE Std. Δχ2/2 CFI TLI RMSEA Hedonic well-being  Life satisfaction 0.388c 0.030 0.406 0.345c 0.030 0.340 3.488c 0.148 0.678 21.245c 0.961 0.933 0.062  Hopelessness 0.238c 0.022 0.260 –0.484c 0.025 –0.464 –3.182c 0.144 –0.592 5.965 0.971 0.950 0.051 Risk behaviour  Delinquent behaviour 0.489c 0.026 0.408 –0.031a 0.013 –0.057 –0.245b 0.079 –0.086 0.16 0.972 0.952 0.047  Problematic internet use 0.223c 0.020 0.243 –0.023c 0.005 –0.107 –0.240c 0.032 –0.215 1.779 0.971 0.950 0.047  Self-harm behaviour 0.084c 0.019 0.105 –0.009c 0.002 –0.114 –0.085c 0.012 –0.214 1.579 0.970 0.949 0.048  Suicidal behaviour 0.109c 0.017 0.143 –0.036c 0.004 –0.240 –0.289c 0.023 –0.367 1.971 0.972 0.952 0.047 Temporal stability Intercept effect Slope effect Model fit Unstd. SE Std. Unstd. SE Std. Unstd. SE Std. Δχ2/2 CFI TLI RMSEA Hedonic well-being  Life satisfaction 0.388c 0.030 0.406 0.345c 0.030 0.340 3.488c 0.148 0.678 21.245c 0.961 0.933 0.062  Hopelessness 0.238c 0.022 0.260 –0.484c 0.025 –0.464 –3.182c 0.144 –0.592 5.965 0.971 0.950 0.051 Risk behaviour  Delinquent behaviour 0.489c 0.026 0.408 –0.031a 0.013 –0.057 –0.245b 0.079 –0.086 0.16 0.972 0.952 0.047  Problematic internet use 0.223c 0.020 0.243 –0.023c 0.005 –0.107 –0.240c 0.032 –0.215 1.779 0.971 0.950 0.047  Self-harm behaviour 0.084c 0.019 0.105 –0.009c 0.002 –0.114 –0.085c 0.012 –0.214 1.579 0.970 0.949 0.048  Suicidal behaviour 0.109c 0.017 0.143 –0.036c 0.004 –0.240 –0.289c 0.023 –0.367 1.971 0.972 0.952 0.047 a p < 0.05; bp < 0.01; cp < 0.001; Δχ2/2 refers to the chi-square change by constraining both intercept effect and slope effect equal across two gender groups. Regarding risk behaviour, the initial levels of MIL predicted reduced delinquent behaviour, problematic internet use, self-harm behaviour and suicidal behaviour at Grade 12, adjusting their initial levels, respectively. Additionally, change in MIL negatively predicted these risk behaviours at Grade 12, adjusting their initial levels. In other words, the smaller the decrease in MIL over time, the lower the level of risk behaviour engagement at Grade 12. All models fitted the data well. Multi-group analyses found no gender differences in the intercept effects and slope effects except the life satisfaction model (Δχ2 (1) = 22.405, p < 0.001). Though the slope effect of MIL on life satisfaction was significant across male and female groups, the magnitude was stronger in the females (B = 3.380, SE = 0.316, β = 0.675, p < 0.001) relative to the males (B = 1.958, SE = 0.165, β = 0.472, p < 0.001). In other words, the decrease in MIL over time had a stronger impact on female adolescents’ life satisfaction relative to male adolescents. Besides studying the outcomes separately, we conducted supplementary analyses using one model examining the latent factor of hedonic well-being with life satisfaction and hopelessness as observed variables and the other model examining the latent factor of risk behaviour with the four risk behaviours as observed variables. The results remained largely the same. In the model of hedonic well-being (CFI = 0.975, TLI = 0.953, RMSEA = 0.047), the initial level of MIL (B = 0.581, SE = 0.056, β = 0.953, p < 0.001) and the change in MIL (B = 2.147, SE = 0.105, β = 0.799, p < 0.001) positively predicted well-being at Grade 12, adjusting its initial level (β = –0.021, SE = 0.078, p > 0.05). In the model of risk behaviour (CFI = 0.953, TLI = 0.927, RMSEA = 0.042), the initial level of MIL (B = –0.035, SE = 0.012, β = –0.116, p < 0.001) and change in MIL (B = –0.454, SE = 0.061, β = –0.325, p < 0.001) negatively predicted risk behaviour, adjusting its initial level (B = 0.462, SE = 0.057, β = 0.401, p < 0.001). Taken together, the level of MIL at Grade 7 and the change in MIL over time both contributed to the hedonic well-being and risk behaviour at Grade 12. Discussion The MIL and how this construct contributes to adolescent well-being have gained surging interest in the field of social work and other fields of youth research and service (Cheon and Canda, 2010; Holloway and Moss, 2010; Shek, 2012). However, the use of meaning-related practices in adolescents in social work is sparse (see Benavides, 2017). One of the reasons is the scarcity of potent empirical evidence showing the benefits of MIL for adolescents’ holistic development. Therefore, this pioneer study addressed an important question regarding whether MIL fosters subsequent adolescent well-being using six waves of data. As expected, we found that adolescents’ MIL at Grade 7 predicted increased hedonic well-being and reduced risk behaviour at Grade 12. Furthermore, we found that MIL decreased from Grades 7 to 11, and adolescents who experienced a faster decrease in MIL over time had poorer hedonic well-being and more risk behaviour at their graduation year. Most of these relationships did not vary across gender, suggesting that the positive effects of MIL are true for both male and female adolescents. By using a stringent longitudinal design with time-order effect, our study provides strong evidence for the predictive role of MIL in well-being. It affirms and extends the theoretical understanding on the role of MIL in fostering adolescent well-being, and further consolidates the groundwork for integrating MIL into social work practices, particularly youth practices (Cheon and Canda, 2010; George and Ellison, 2015). Theoretically, these findings provide support for and advance Frankl’s (1959) existential theory and the strength-based approach that regards spirituality as a developmental asset for fostering positive youth development (Benson et al., 2003; Shek and Wu, 2013) and the thesis that regards spirituality as a recovery resource for clients suffering from mental problems (Starnino et al., 2014) in two aspects. First, the presence of longitudinal effects of MIL suggests that MIL is predictive of hedonic well-being and risk behaviours and the effects may last over time. An initial high level of MIL may set a stage for the subsequent growth of well-being. As suggested by Brassai et al. (2011, p. 45), ‘the individuals feel greater presence of meaning when they understand themselves (e.g. self-acceptance), the world around them (e.g. environmental mastery), and their fit within the world (e.g. positive relationships)’. This is a developmental asset fostering one’s positive outlook towards current and future life. Additionally, individuals who have a higher level of MIL presumably have greater capacity to derive meanings from life experiences and better internal resource to cope with mental problems (Fry, 1998; Steger, 2012). Such spiritual strength may also serve as a resiliency factor that protects adolescents from engaging in risk behaviour when they encounter challenges and adversities (Brassai et al., 2011; Kleiman and Beaver, 2013). Particularly, this study provides initial evidence for the long-term protection for adolescent risk behaviours, including delinquent behaviour, problematic internet use, suicidal behaviour and self-harm behaviour, which fills the void between the decades of theoretical argument for the important effect of MIL in diminishing risk behaviour and the empirical support. The heightened hedonic well-being and lessened risk behaviour may, in turn, sustain MIL, which forms a virtuous circle. Therefore, the positive effect of MIL would be carried over from early adolescence to late adolescence. Our findings support the argument for the enduring effect of MIL (Baumeister et al., 2013). Life meaning that integrates the past and future is more likely to produce long-term effects on constructing a better life. Altogether, our study makes a significant contribution to enriching social workers’ understanding of youth well-being and risk behaviour by providing an additional perspective based on the existential approach. It is noteworthy that MIL declines over high-school years, and this developmental change has a psychological impact on adolescent well-being beyond that of the MIL level. Adolescents who experience a faster drop in life meaning over time would suffer greater cost to their hedonic well-being and behavioural adjustment. The greater deterioration in adolescent spiritual strength possibly reflects increasing existential challenges (e.g. difficulty in identifying meaningful life goals) yet insufficient support in adolescents’ lives. The accumulation of existential frustration over time probably leads to undesirable developmental outcomes. The current findings indicate a need to expand the scope of MIL theories by considering its dynamic nature and to provide more empirical evidence that explicates the developmental process of MIL over the lifespan. These insights also bring important practical implications for social workers. First, our findings reinforce the call for integrating spirituality into social work practice (Holloway and Moss, 2010; George and Ellison, 2015; Oxhandler and Pargament, 2014). When people encounter life challenges that possibly render them physically or psychological suffering, spirituality can be used to heal the pain (Starnino et al., 2014; Benavides, 2017). Indeed, MIL can be changed, as evidenced by youth programmes that help adolescents identify their life meaning (Ma and Shek, 2017) and meaning therapy that guides clients to establish enduring meaning and coax values from their life events and circumstances (Vos et al., 2015). As asserted by the theorist and practitioner of meaning therapy Paul Wong (1999), ‘human beings are capable of change and personal growth through learning’ (p. 48). Additionally, a meta-analysis of six meaning therapies on adult patients with serious diseases has demonstrated strong evidence in its effects on enhancing self-efficacy and reducing psychopathology (Vos et al., 2015). However, the application of spirituality-oriented practices still leaves behind adolescent clients (Cheon and Canda, 2010). It is still debatable whether it applies to adolescents who might be too young to wrestle with issues transcending beyond self and mundane lives (see Fitzgerald, 2005; Benavides, 2017). Our findings together with increasing effectiveness evidence of meaning practices (Vos et al., 2015) provide a suggestion to social workers and youth workers that it is worth integrating adolescent clients’ spirituality into the practice when dealing with their mental health. Helping adolescents coax meanings from their life experiences may also be effective to boost their hedonic well-being and reduce their risk behaviour. Furthermore, our findings highlight the need to address adolescent clients’ change of subjective experience in MIL. Adolescents who have experienced a rapid drop in the feeling of meaningfulness might also be at risk. Therefore, social work professionals and youth workers may need to consider such ‘change’ in their clients. Finally, considering social workers generally lack adequate training in spirituality (Holloway, 2007), we call for integrating spiritual constructs and spirituality-based practice into social work curricula. Social work institutes should provide front line social workers with more opportunities to receive related training and supervision. Despite its pioneer nature, there are four limitations of the study. First, this study only focused on perceived life meaning. Besides that, other related dimensions such as sense of congruence (Steger, 2012) are worth studying. Second, the measurement may limit the understanding of the outcomes. We relied on self-report for all the measures, because MIL and hedonic well-being tap into subjective feelings and experiences, while risk behaviours of deliberate self-harm and suicide are private. Yet, it is still desirable to involve reports of significant others and use objective measures, as it would give a more holistic picture about the inquiry. Additionally, the reliability of suicidal behaviour was not high, probably because of the discrepancy in the occurrence rate between suicidal ideation (Grade 7: 13.7 per cent) and suicidal plan (Grade 7: 4.9 per cent)/attempt (Grade 7: 4.7 per cent). We recommend future studies to examine suicidal ideation and suicidal behaviour separately with detailed scales. Third, the current sample was limited to Hong Kong Chinese adolescents. According to the small body of evidence derived from mainland Chinese youngsters (Wang et al., 2016; Zhang et al., 2015) showing that MIL was positively associated with hedonic well-being and negatively associated with risk behaviour, it is highly likely that the current findings can be generated into other Chinese adolescents. However, further studies with Chinese and non-Chinese samples are needed to replicate our findings. Lastly, the longitudinal design cannot fully demonstrate the causal relationship between life meaning and adolescent developmental outcomes in the intervention contexts, and thus further studies are needed to investigate practice-related factors in the intervention contexts. Nevertheless, the present study generated pioneer findings regarding the role of change in life meaning in adolescent well-being, which has practical social work implications. Funding The Project P.A.T.H.S. and this paper are financially supported by the Hong Kong Jockey Club Charities Trust. 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Published: Apr 25, 2018

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