Over the recent decades, the positive aspect of mental health has drawn much public attention (Arslan & Allen, 2020; O’Reilly et al., 2018). Traditionally, mental health was conceptualised as the absence of mental health problems, predominantly from a deficit-based perspective (Renshaw et al., 2016; Westerhof & Keyes, 2010). This view has its roots in psychopathology research and has been influential in shaping the diagnostic classification and treatment approaches (Christmas & Khanlou, 2019). However, this deficit-based perspective alone has been criticised as unidimensional and hence insufficient to capture a comprehensive landscape of mental health (Christmas & Khanlou, 2019; Kobau et al., 2011). According to the World Health Organization (2022), mental health goes beyond merely the absence of diseases or disorders and involves both positive and negative dimensions—a mentally healthy individual is able to manage different stressors, develop his/her potential, study and work effectively, and give back to the community. Cumulative evidence has shown a dual-factor model of mental health, in which positive and negative indicators are equally valid and essential (Antaramian et al., 2010; Keyes, 2005; Kobau et al., 2011). Notably, positive psychological traits (e.g., gratitude and optimism), as suggested by many researchers, are considered as the positive dimension of mental health (e.g., Arslan, 2019a; Jones et al., 2013; Renshaw et al., 2014). In the early 2000s, some researchers such as Park, Peterson, and Seligman advocated research into human character strengths (Park & Peterson, 2006; Park et al., 2004; Seligman, 2002), focusing on determining and evaluating positive traits and their association with resilience and coping. Following this call, subsequent studies revealed positive relationships between certain positive traits and human thriving and flourishing (Diener, 2009; Hausler et al., 2017; Seligman, 2002). Specifically, gratitude, hope/optimism, love, persistence, and zest were found to be most significantly associated with life satisfaction in adult (Buschor et al., 2013; Park et al., 2004) and youth populations (Park & Peterson, 2006; Toner et al., 2012). In addition, these positive traits serve as good indicators of social and emotional health for children and adolescents (Wang et al., 2018, 2021). Given the association between these positive traits and desirable psychological outcomes, researchers and education practitioners have developed and evaluated trait-based interventions to foster flourishing in individuals, such as gratitude journaling (e.g., Bohlmeijer et al., 2021) and optimism-induction (e.g., Denis & Odgden, 2022).
In recent years, a new line of research has shifted the focus from studying isolated positive psychological traits to examining the potential accumulative effects of multiple positive psychological traits (Renshaw & Bolognino, 2016). A central concept, covitality, has emerged from this line of research, emphasising an integrative approach to understanding the manner in which positive psychological traits co-occur to promote human well-being (Furlong et al., 2013; Renshaw & Bolognino, 2016). This development calls for further exploration to probe into potential routes from covitality to flourishing (Furlong et al., 2013; Jones et al., 2013). Given the increasing evidence supporting covitality as a better predictor than individual positive psychological traits, it is essential to investigate how certain positive traits may co-exist and to what extent their concurrence contributes to human flourishing. Therefore, the current study explored how student covitality, a construct defined by Furlong et al. (2013) to describe the combination of four school experience-grounded positive psychological traits (i.e., gratitude, optimism, zest, and persistence), predicted flourishing through resilience and prosocial behaviour among primary school students in the Hong Kong context.
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1 Positive Psychological Traits, Student Covitality, and Flourishing
Grounded in resilience theory, Ostaszewski and Zimmerman (2006) suggested a cumulative-factor framework that takes into account the combined effects of both cumulative risks and assets in predicting youth’s well-being. This framework posits that individuals are more likely to experience better well-being when they possess more assets and encounter less risks. Positive psychology research has adopted the cumulative-factor framework for studying positive psychological traits, commonly known as “character strengths” (Peterson & Seligman, 2004), and their relation to happiness and life satisfaction (Park & Peterson, 2006). Along this line, researchers have identified several positive psychological traits that later paved the way for the development of covitality research (Furlong et al., 2013; Renshaw & Bolognino, 2016). Covitality research emphasises the importance of considering multiple, co-occurring positive psychological traits and the accumulative effects in promoting resilience, happiness, life satisfaction and so forth (Renshaw et al., 2014).
Existing literature seems to suggest that covitality can be a better predictor of human flourishing. For example, Jones et al. (2013) preliminarily examined the concept of covitality by integrating five traits, namely hedonia (conceptualised as the pursuit of happiness), hope, gratitude, self-efficacy (conceptualised as a dispositional expectancy regarding a person’s ability), and optimism. Jones et al. (2013) found that covitality as a whole of these five traits better predicted personal adjustment and internalising problems such as stress and anxiety than simply considering the five traits independently. Furthermore, Furlong et al. (2013) suggested that four pivotal positive psychological traits, namely gratitude, optimism, zest, and persistence, were highly related to psychosocial outcomes among children and adolescents. Therefore, Furlong and colleagues (2013) then developed the construct, student covitality, to indicate the combination of the four co-occurring positive psychological traits within the school context. Over the past decade, researchers have examined the construct of student covitality across cultures such as Australia (Wilkins et al., 2015), China (Wang et al., 2018), Japan (Iida et al., 2021), South Korea (Kim et al., 2019), the United States (Renshaw, 2017) and Turkey (Arslan & Allen, 2020). Advocates of student covitality research suggested that it can be a used as a universal screening tool to assess students’ mental health status, as students who report low covitality are not always identified by deficit-based measures (Kim & Choe, 2022; Kim et al., 2019; Wang et al., 2018). In general, researchers have established positive links between student covitality and a wide array of psychosocial outcomes such as subjective well-being, personal adjustment, prosociality and depression among children and adolescents (Arslan, 2019a, b; Kim et al., 2019; Wang et al., 2018).
Nevertheless, it remains unexplored whether student covitality as an integrative construct is superior to individual traits in predicting flourishing in the Chinese context, and if so, what the pathways may be. We argue that resilience, one’s capacity to adapt well in adverse circumstances (Smith et al., 2008) and prosocial behaviour, voluntary social actions performed for the benefit of others (Eisenberg et al., 2006) could be two potential variables that play a role in our hypothesised link between student covitality and flourishing.
2 Resilience as a Potential Mediator Between Student Covitality and Flourishing
Literature supporting resilience as a predictor of flourishing can be drawn from studies that examine the relationship between resilience and subjective well-being (Moreira et al., 2021) and the effectiveness of resilience-related intervention programmes (Dray et al., 2017). For example, Dray et al. (2017) systematically reviewed 57 randomised controlled trials of resilience-focused school-based interventions and found that strengthening resilience were effective in reducing mental health problems for children and adolescents. Notably, compared with risk factors (e.g., anxiety and depression), protective factors such as optimism and positive affect seem to be better predictors of resilience (Lee et al., 2013; Martínez-Martí & Ruch, 2017). According to Smith et al. (2008), resilience is an individual’s ability to recover from difficulties, a process which is influenced by resilience resources. Therefore, a plausible hypothesis to consider is that this recovery ability may function as a mediator between resilience resources (e.g., positive psychological traits) and flourishing outcomes.
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Previous studies have suggested that resilience can be a mediator in the associations between individual positive psychological traits (e.g., gratitude and optimism) and flourishing outcomes. Specifically, in a sample of Chinese elementary school pupils, Kong et al. (2021) found that gratitude had an indirect effect on life satisfaction and affective well-being through resilience. Similarly, Caleon et al. (2019) also found that school resilience was a partial mediator between gratitude and school well-being among adolescents. In addition, optimism was found to indirectly affect life satisfaction and well-being through resilience (Miranda & Cruz, 2020; Zayas et al., 2021). In a recent large-scale longitudinal study (three-wave over six months) across Australia, Canada, the United Kingdom, and the United States (N = 3,710), higher scores on zest and persistence significantly predicted higher resilience as well as less depression and functional impairment (Blanchard et al., 2021). To date, there have not been studies on the conceivable mediating effect of resilience between the integrative construct of student covitality and flourishing.
3 Prosocial Behaviour as a Potential Mediator Between Student Covitality and Flourishing
Engagement in prosocial behaviour can be conducive to subjective well-being (Yang et al., 2017; Son et al., 2020). From the perspective of positive psychology interventions, the “well-doing” of prosocial behaviour would promote “well-being” through children’s developing awareness of their emerging strengths in the formation of their self-identity (Lottman et al., 2017, p. 84). Previous studies have reported associations between prosocial behaviour and different psychosocial outcomes, such as internalising problems (e.g., anxiety and depression) and externalising problems (e.g., conduct problems; Arslan, 2019a), school belonging and engagement (Arslan, 2019a; Wilkins et al., 2015). A meta-analysis of 27 experimental studies (total N = 4045) reported a small-to-medium effect of prosocial behaviour on subjective well-being (Curry et al., 2018). For example, in a six-week randomised controlled trial, Nelson et al. (2016) found that participants who performed more prosocial behaviour showed improvements in psychological flourishing than those who engaged in self-focused or neutral behaviour. In addition, some longitudinal studies found a reciprocal relationship between prosocial behaviour and subjective well-being among primary school students (Chen et al., 2020; Su et al., 2021). In terms of the connection to student covitality, prosocial behaviour was moderately-to-largely correlated with the four positive psychological traits (i.e., gratitude, optimism, zest, and persistence) respectively as well as student covitality as a whole across cultures (e.g., Arslan, 2019a; Wang et al., 2018; Wilkins et al., 2015).
Positive psychological traits can be factors that motivate individuals to engage in prosocial behaviour. For instance, Froh et al. (2010) found that school-based gratitude interventions led to increased subsequent prosocial behaviour among adolescents. Post (2005) suggested that when individuals engage in actions that positively affect others, they experience increased positive emotions, a sense of purpose and meaning, and improved social connections to others. Existing studies have established positive links between optimism, prosocial behaviour and life satisfaction (Froh et al., 2009). Moreover, a study by Tian et al. (2015) found that prosocial behaviour partially mediated the relationship between gratitude and subjective well-being in school (e.g., school satisfaction and positive affect) among Chinese primary school students. Although these studies have suggested that individual positive psychological traits may lead to increased prosocial behaviour and then to desirable psychosocial outcomes, there is little evidence showing the potential mediating role of prosocial behaviour in the relationship between student covitality and flourishing.
4 The Current Study
Taken together, existing evidence suggests associations of the four positive psychological traits (i.e., gratitude, optimism, zest, and persistence) with subjective well-being, as well as with resilience and prosocial behaviour. Moreover, research has also shown the effect of resilience and prosocial behaviour on promoting human flourishing (Dray et al., 2017; Nelson et al., 2016). Student covitality, an integrative construct of the four positive psychological traits grounded in the school context, seems to be a better indicator of positive life outcomes, as the combination of the four individual traits could lead to a greater impact on human flourishing (Furlong et al., 2013; Ostaszewski & Zimmerman, 2006; Renshaw et al., 2014). While previous studies have examined the associations between student covitality and positive psychosocial outcomes (e.g., Arslan, 2019a, b; Kim et al., 2019; Wilkins et al., 2015), potential mediators such as resilience and prosocial behaviour remain unexplored. Kaplan (2017) also suggested that well-being is a broad concept which can be indicated by both “private” (i.e., personal) and “public” (i.e., social) experiences (p. 2). Hence, it seems plausible to further investigate the pathways from student covitality to flourishing through resilience (as a personal attribute) and prosociality (as an interpersonal-social attribute). In the current study we aimed to test the proposed model (see Fig. 1) with the following hypotheses:
Fig. 1
Proposed model of student covitality, resilience, prosocial behaviour, and flourishing
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Hypothesis 1
Student covitality would be positively related to flourishing.
Hypothesis 2
Resilience would mediate the relationship between student covitality and flourishing.
Hypothesis 3
Prosocial behaviour would mediate the relationship between student covitality and flourishing.
5 Method
5.1 Participants
The sample consisted of 1107 Chinese students (50.4% females) from 34 primary schools in Hong Kong (7.0% Hong Kong Island, 36.4% Kowloon, and 56.5% New Territories). There were 401 pupils from Grade 4 (36.2%), 363 pupils from Grade 5 (32.8%), and 343 pupils from Grade 6 (31.0%), respectively1. Regarding the religious background of the schools, 25.8% were Catholic, 24.1% were Christian, 13.6% were Buddhist, 8.6% were Taoist, and 27.9% were secular.
5.2 Procedures
The current study was part of a larger study investigating students’ perceptions of values and well-being in Hong Kong (Kuang et al., 2023). The current study adopted a convenience sampling method. Prior to data collection from April to June in 2018 (second semester of the 2017-18 school year), ethics approval was obtained from the Human Research Ethics Committee of the affiliated University (masked for blind review). Invitations were sent to primary schools in Hong Kong with an information sheet about project aims and procedures. All participants were informed about confidentiality and assured of their right to withdraw from the study at any time without negative consequences. After obtaining consent from the school principals, each participating school assigned at least one responsible teacher for data collection, and at least one class of students in each grade (i.e., Grades 4 to 6) was invited. The teachers in-charge ensured that only students whose individual consent and consent from their legal guardians were obtained participated in the current study. Data were collected through paper-and-pencil questionnaires. Participants completed the questionnaire in their schools.
5.3 Measures
5.3.1 Flourishing Scale (FS)
The 8-item FS was used to assess participants’ self-perception of several key areas of human functioning (e.g., meaning and purpose in life, interpersonal relationships, self-competence; Diener et al., 2010). The FS suits collectivistic cultures that emphasise interpersonal relationships and contributions to society (Ho et al., 2014; Tang et al., 2016). Participants rated items on a 7-point Likert scale from 1 (strongly disagree) to 7 (strongly agree), with a higher total score indicating a higher level of flourishing. A sample item of the FS is “I am a good person and live a good life”. Overall, the FS showed good psychometric properties in Chinese populations (e.g., Duan & Xie, 2016; Tang et al., 2016; Tong & Wong, 2017), with Cronbach’s alphas ranging from .88 to .93 and a consistent single-factor structure. In the present study, the Cronbach’s α of the FS was .90.
5.3.2 Positive Experiences at School Scale (PEASS)
The 16-item PEASS (Furlong et al., 2013) to was used to assess student covitality on a 4-point Likert scale from 1 (never) to 4 (always). The PEASS is comprised of four 4-item subscales, namely gratitude (e.g., “I feel thankful for my good friends at school.”), optimism (e.g., “I expect to feel happy in class.”), zest (e.g., “I get excited when I learn something new at school.”), and persistence (e.g., “I keep working until I get my schoolwork right.”), with a higher total score indicating a higher level of student covitality. The PEASS had good psychometric properties across cultures (e.g., Arslan & Allen, 2020; Wang et al., 2018; Wilkins et al., 2015). Generally, the PEASS showed overall good internal consistency (Cronbach’s alphas ranged from .87 to .94), with acceptable reliability for the four subscales (Cronbach’s alphas ranged from .66 to .81; Arslan, 2019a; Arslan & Allen, 2020; Fang et al., 2021; Furlong et al., 2013). In the present study, the Cronbach’s alpha values were .93 for the PEASS, .83 for gratitude, .83 for optimism, .82 for zest, and .78 for persistence, respectively.
5.3.3 Prosocial Behaviour Scale (PBS)
The 4-item PBS (Furlong et al., 2013) was used to measure prosocial behaviour within the school settings. Participants rated items on a 4-point Likert scale from 1 (never) to 4 (always). A sample item of the PBS is “I am nice to other students”. The PBS also showed adequate internal consistency (Cronbach’s alphas ranged from .72 to .80; Arslan, 2019a; Furlong et al., 2013). The Cronbach’s α of the PBS was .84 in the current study.
5.3.4 Brief Resilience Scale (BRS)
The 6-item BRS was used to measure participants’ ability to rebound from stress (Smith et al., 2008), on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). A sample item of BRS is “I tend to bounce back quickly after hard times”. After reverse coding, a higher total score indicates a higher level of resilience. Generally, the BRS showed good internal consistency with Cronbach’s alphas ranging from .80 to .91 (Smith et al., 2008) and has been validated in Chinese populations (e.g., Cronbach’s α = .71; Fung, 2020). The Cronbach’s α of the BRS was .67 in the current study.
5.4 Data Analysis
After the normality check, confirmatory factor analysis (CFA) was used to examine the measurement model with all the cross loadings fixed to zero. A series of multigroup CFAs were used to test invariance across gender and grade. Measurement invariance was tested by using the following models: configural, metric, scalar, and strict. The Δχ2 test was non-significant, and the ΔCFI was less than .01, indicating invariance of the measurement models (Cheung & Rensvold, 2002). Structural equation modelling (SEM) was then used to test the mediating effect of resilience and prosocial behaviour on the relationship between student covitality and flourishing (see Fig. 1). To reduce the measurement error, the BRS was divided into three parcels, using the factorial algorithm parcel-building method (Rogers & Schmitt, 2004). Using Little’s missing completely at random (MCAR) test, the data met the MCAR assumption (χ2 (df = 1061) = 1105.60, p = .17). Thus, full information maximum-likelihood was used to handle missing data in lavaan (Hartley & Hocking, 1971). Based on the fact that most data violated normality (Micceri, 1989), robust maximum likelihood was used to estimate model parameters and model fit of CFA and SEM models (Arbuckle, 1996; Li, 2016). The threshold limits for model fit proposed by Hu and Bentler (1999) and Brown (2015) are: χ2/df < 3; comparative fit index (CFI) > .90; Tucker-Lewis index (TLI) > .90; standardised root-mean-square residual (SRMR) < .09; root-mean-square error of approximation (RMSEA) < .06, 90% CI < .06. All the analyses were conducted using R script (R Core Team, 2021): the package lavaan for computing CFA and SEM (Rosseel, 2012) and the package tidySEM for plotting SEM.
6 Results
6.1 Descriptive Analysis
Table 1 reports the descriptive statistics of the measures, including the number of items, mean values, standard deviations, range, Cronbach’s alpha, skewness, and kurtosis. The Cronbach’s α for all measures were above .60, which are sufficient for psychological measures suggested by Kline (1999). All the variables were within the range of -1 to 1 skewness and kurtosis, indicating slight violation of normality (Lei & Lomax, 2005). Multivariate normality was examined by Mardia’s multivariate normality test (Mardia, 1970, 1974), Henze-Zirkler’s multivariate normality test (Henze & Zirkler, 1990), and Doornik-Hansen’s multivariate normality test (Doornik & Hansen, 2008). All the tests were statistically significant, suggesting a violation of multivariate normality. Thus, robust maximum likelihood was used to provide robust estimates.
Table 1
Descriptive statistics, cronbach’s alpha, and skewness and kurtosis of the study variables
Variable | Number of items | M | SD | Range | Cronbach’s α | Skew. | Kurt. |
|---|---|---|---|---|---|---|---|
Flourishing | 8 | 41.49 | 9.31 | 8–56 | .90 | -.45 | -.10 |
Student covitality | 16 | 48.76 | 9.87 | 16–64 | .93 | -.47 | -.18 |
Gratitude | 4 | 12.30 | 2.95 | 4–16 | .83 | -.54 | -.40 |
Optimism | 4 | 12.78 | 2.81 | 4–16 | .83 | -.87 | .33 |
Zest | 4 | 11.03 | 3.18 | 4–16 | .82 | -.13 | -.81 |
Persistence | 4 | 12.63 | 2.69 | 4–16 | .78 | -.66 | -.08 |
Prosocial behaviour | 4 | 13.22 | 2.52 | 4–16 | .84 | -.76 | .04 |
Resilience | 6 | 20.26 | 4.17 | 6–30 | .67 | .22 | .03 |
Table 2 demonstrates the correlations among the first-order variables, ranging from .23 to .71. The intercorrelations among the latent variables did not exceed the cut-off value of .90 (Tabachnick & Fidell, 2013), indicating that the discriminant validity was well established. All loadings ranged from the lowest .60 to .91 and were significant and above the cut-off of .25 (Schreiber et al., 2006), indicating that all measures were reliable.
Table 2
Correlations among study variables
Variable | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|---|---|---|---|---|---|---|---|
1. Flourishing | 1 | |||||||
2. Gratitude | .58*** | 1 | ||||||
3. Optimism | .58*** | .71*** | 1 | |||||
4. Zest | .56*** | .69*** | .61*** | 1 | ||||
5. Persistence | .61*** | .58*** | .52*** | .58*** | 1 | |||
6. Prosocial behaviour | .55*** | .54*** | .48*** | .48*** | .67*** | 1 | ||
7. Resilience | .43*** | .23*** | .31*** | .27*** | .34*** | .28*** | 1 | |
8. Student covitality | .68*** | .89*** | .84*** | .86*** | .79*** | .63*** | .33*** | 1 |
6.2 Measurement Model
To examine how well each latent variable was manifested by its observed measures, CFA was used to assess the measurement models before conducting mediational models. There were four latent variables including flourishing, student covitality, prosocial behaviour, and resilience. Student covitality, as a second-order construct, was validated in a Chinese sample by Wang et al. (2018). To improve the psychometric properties of the variables and simplify the model without losing information, parcelling was used for resilience based on the factorial algorithm method (Rogers & Schmitt, 2004). In order to ensure every latent variable has at least three measure variables, three parcels were formed out of six items: (1) 1st and 6th highest factor loadings (BRS 1 and BRS 4); (2) 2nd and 5th highest loadings (BRS 3 and BRS 6); and (3) 3rd and 4th highest loadings (BRS 5 and BRS 2).
The CFA analysis showed that the measurement model of student covitality indicated a good fit to the data: χ2 = 1486.724, df = 425, CFI = .918, TLI = .911, RMSEA = .055 (90% CI = [.052,.058]), SRMR = .056. All the factor loadings of the measurement variables were significantly loaded on the latent variables and ranged from .60 to .91, suggesting that the measurement models were well established. In addition, the measurement invariance was examined by a series of multigroup SEM. The results indicated that this model had sufficient invariance across gender and grade (see details in supplementary Tables S1-S2).
6.3 Mediation Model
To test the hypotheses, the mediational model we proposed (see Fig. 2) indicated an adequate model fit. In line with Hypothesis 1 (direct effect), student covitality was positively associated with flourishing (β = .594, p < .001), prosocial behaviour (β = .691, p < .001), and resilience (β = .407, p < .001). Flourishing was positively associated with resilience (β = .272, p < .001) and prosocial behaviour (β = .090, p < .05). The percentages of variance explained were 82.3% for gratitude, 69.6% for optimism, 77.8% for zest, 71.9% for persistence, 16.6% for resilience, 47.7% for prosocial behaviour, and 65.5% for flourishing. In addition, corresponding to Hypothesis 2 and 3, the indirect effects of student covitality on flourishing through resilience (β = .111, p < .001) and through prosocial behaviour (β = .062, p < .05) were significant. Table 3 shows the direct, indirect, and total effects of the mediation model.
Fig. 2
Mediational model with standardised coefficients. This structural equation model predicts flourishing from student covitality, with mediating effects of resilience and prosocial behaviour. Note. Statistics are standardised regression coefficients. All the paths are significant in this study. For easy viewing, residual and measurement errors are omitted. *** p < .001. ** p < .01. * p < .05
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Table 3
Direct, indirect, and total effects of the mediational model
Paths | Unstandardised coefficient b | Standardised coefficient β | SE | p |
|---|---|---|---|---|
Direct paths | ||||
Covitality → Flourishing | 1.044 | .594 | .097 | .000 |
Covitality → Resilience | .747 | .407 | .084 | .000 |
Covitality → Prosocial behaviour | .668 | .691 | .041 | .000 |
Resilience → Flourishing | .260 | .272 | .033 | .000 |
Prosocial behaviour → Flourishing | .164 | .090 | .078 | .036 |
Indirect paths | ||||
Covitality → Resilience → Flourishing | .194 | .111 | .030 | .000 |
Covitality → Prosocial behaviour → Flourishing | .110 | .062 | .051 | .033 |
Total effects | 1.348 | .767 | .078 | .000 |
The bootstrap method was used to compute the confidence interval of indirect effects using the adjusted bootstrap percentile method (BCa). The sample was set to 5000. Corresponding to Hypothesis 2, resilience mediated the effect of student covitality on flourishing. The indirect path from student covitality to flourishing (student covitality → resilience → flourishing) was statistically significant (unstandardised beta b = .194, standardised beta β = .111, p < .001, 95% CI for b [.142, .262]). Corresponding to Hypothesis 3, prosocial behaviour mediated the effect of student covitality on flourishing. The indirect effect (student covitality → prosocial behaviour → flourishing) was negligibly small (standardised beta β between .01 and .09 is a small effect size according to Cohen’s rule of thumb suggested by Kenny, 2023), but significant (unstandardised beta b = .110, standardised beta β = .062, p < .05, 95% CI for b [.006, .214]). The direct effect (student covitality → flourishing) was statistically significant (unstandardised beta b = 1.044, standardised beta β = .594, p < .001, 95% CI for b [.867, 1.254]). The alternative model constrained the two indirect paths to equal and compared with this mediational model. The result showed ∆S-B2 = 14.225 (∆df = 1), p < .001, indicating that the coefficients for the two indirect paths were not equal. Taken together, the effect of student covitality on flourishing was partially mediated by resilience and prosocial behaviour. That is, a higher level of student covitality led to greater resilience and more prosocial behaviour, which in turn led to a higher level of flourishing.
7 Discussion
The concept of covitality emphasises the importance of understanding the way in which combinations of certain co-occurring positive psychological traits promote human well-being (Furlong et al., 2013; Renshaw & Bolognino, 2016). As suggested by pioneers in covitality research, more studies are expected to examine how an individual’s covitality is related to flourishing (Furlong et al., 2013; Jones et al., 2013; Renshaw et al., 2014). Previous studies have revealed the positive relationship between student covitality and flourishing outcomes (e.g., Arslan, 2019a, b; Wang et al., 2018), but the potential pathways from student covitality to flourishing were largely unknown. Building upon empirical evidence supporting resilience (e.g., Kong et al., 2021; Miranda & Cruz, 2020) and prosocial behaviour (e.g., Tian et al., 2015) as mediators, the current study proposed a mediational model of student covitality predicting flourishing through resilience and prosocial behaviour among primary school students in Hong Kong. The current study corroborates with and extends from research on student covitality (Furlong et al., 2013; Renshaw et al., 2014). The results revealed that student covitality positively predicted flourishing (H1), with resilience and prosocial behaviour partially mediating the relationship between student covitality and flourishing (H2 & H3). While there was a medium direct effect of student covitality on flourishing, the indirect effects via resilience and prosocial behaviour were medium and small, respectively. Following the line of covitality research, the current study provides implications for future research and practice in promoting children’s flourishing.
7.1 Covitality as a Latent Construct
Our findings applying student covitality as a second-order construct to predict flourishing through resilience and prosocial behaviour provided empirical support for the theoretical argument of treating covitality as a latent construct of positive psychological traits (Furlong et al., 2013; Jones et al., 2013; Renshaw et al., 2017). While for the measurement model of student covitality, the first-order construct model showed a better model fit than the second-order construct model, both models showed good model fit (see details in the supplementary Table S3). Based on the parsimony criterion for model selection, previous studies advocated for a second-order latent construct, despite the better model fit of the first-order construct (e.g., Furlong et al., 2013; Jones et al., 2013; Renshaw, 2017). Interestingly, the partial mediation effect of prosocial behaviour in the second-order construct model was not significant in the first-order construct model, providing some support to the utility of the student covitality variable in that the integrative construct which combines the component positive psychological traits may enable us to better examine the pathways to positive developmental outcomes as captured in flourishing than the individual constructs. The finding that prosocial behaviour only serves as a pathway in the latent construct model may suggest that flourishing is only promoted via prosociality in students with more all-round student covitality, not those possessing only individual positive traits. This notion echoes the view that advocates for integrating positive psychological traits because “the sum is greater than the parts” (Furlong et al., 2014, p. 28). Still, there is much to be done in understanding the development course of covitality and effective covitality-focused intervention approaches.
7.2 Student Covitality Predicts Flourishing through Resilience and Prosocial Behaviour
Our findings support and extend previous works showing positive psychological traits predicting students’ well-being (e.g., Park & Peterson, 2006; Toner et al., 2012), and more importantly, highlighting student covitality as a valid predictor of flourishing (Kim et al., 2022; Renshaw, 2017; Wang et al., 2018) particularly through resilience and prosocial behaviour. This integrative approach of conceptualising and measuring covitality (Renshaw et al., 2014; Renshaw, 2017) provides us with a more comprehensive model of positive psychological traits in the school context. Also, this approach offers us a viable way to investigate pathways to flourishing beyond individual psychological traits by adopting a new angle that considers the accumulative effects of multiple positive psychological traits in predicting resilience and subjective well-being (Renshaw et al., 2014).
Resilience, in particular, significantly explains the link between student covitality and flourishing, as student covitality could be a “resilience resource” (Smith et al., 2008, p. 195) that influences one’s ability to rebound from stressful life events and promotes one’s flourishing as a result. Moreover, resilience could be a critical pathway to flourishing, not only from individual positive psychological traits such as gratitude (Kong et al., 2021), optimism (Miranda & Cruz, 2020), and persistence (see details in the supplementary Figure S1), but also from student covitality as a whole representing the combination of the four positive psychological traits (i.e., gratitude, optimism, zest, and persistence). Hence, it may be feasible for school-based resilience-building interventions to target the broad construct of student covitality in addition to the individual psychological traits of gratitude, optimism, and persistence. However, more evidence on such interventions is needed to validate the effect of resilience in connecting student covitality and flourishing outcomes.
Regarding prosocial behaviour, its role in mediating the effects of student covitality on flourishing was significant, albeit small, in the latent construct model but not significant in all the first-order construct models (see details in the supplementary Figure S1), further supporting the strength of conceptualising covitality integratively (e.g., Kim et al., 2022; Renshaw, 2017; Wang et al., 2018) in capturing the mechanisms that drive student flourishing. Preliminarily, we now have empirical evidence supporting that student flourishing may be promoted not only through the enhancement of positive psychological traits of student covitality and personal resilience, but also through nurturing prosocial behaviour. In other words, school environments conducive to the development of student covitality may promote flourishing from both personal (i.e., via resilience) and interpersonal pathways (i.e., via prosocial behaviour; Kaplan, 2017; O’Reilly et al., 2018) which entail both cognitive-affective and behavioural processes. Given that student covitality is constructed on school experience-grounded positive psychological traits (Furlong et al., 2013), it is particularly interesting to reveal this social component of flourishing in that a positive feedback loop may be conceived (see Fig. 3), as the prosocial behaviour engendered from student covitality in a positive school environment may very well create further drivers for a vibrant school community that nurtures flourishing of students (Chen et al., 2020; Su et al., 2021). For example, Ellis et al. (2016) found that implementing a school-wide intervention in increasing prosocial behaviour through engaging students in meaningful roles within the school community led to positive effects on school climate and students’ social and academic outcomes. While further validation of these implications is needed, our findings provide possible directions for future empirical investigations.
Fig. 3
A potential feedback loop involving student covitality, prosocial behaviour, and flourishing
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7.3 Student Covitality as a Universal Screening Tool for Mental Health
To date, over 20 countries have adopted the concept of covitality and used covitality-related measures for research, education, and clinical practice (see Project Covitality for an overview; University of California Santa Barbara, n.d.). As the traditional deficit-based approach alone is insufficient to capture the complete mental health status (Dowdy et al., 2015; Keyes, 2005; Kim & Choe, 2022), researchers have validated and used the Social and Emotional Health Survey–Primary (originally named the PEASS) as a universal screening tool to assess and monitor students’ mental health status from whole-school-level (e.g., Renshaw, 2017; Wang et al., 2018) to city-level (e.g., Castro-Kemp et al., 2020; Kim & Choe, 2022; Kim et al., 2019). Results showed that applying the student covitality measure helped identify students who needed support but might not be identified by deficit-based measures (Castro-Kemp et al., 2020; Kim & Choe, 2022; Renshaw, 2017).
The current study was conducted in Hong Kong, a Special Administrative Region of China where Chinese and Western cultures co-exist and mix (Yee, 2001). In comparison with the average level of student covitality in other cultures, the participants in the current study seemed to show a higher level than a sample in Korea (Kim et al., 2019), but scored lower than some other samples in the United States (Renshaw, 2017), Australia (Wilkins et al., 2015), and Turkey (Arslan, 2019a; Arslan & Allen, 2020), and a sample in mainland China (Wang et al., 2018). In view of the medium predictive effects of covitality on flourishing and the relatively low level of covitality in our sample, more research and intervention efforts are warranted to understand the drivers of covitality better and thus support flourishing in the school context. Previous studies have suggested that launching school-based programmes can be a feasible way to promote covitality (Yang, 2022). For instance, Fang et al. (2021) developed an eight-week psychoeducation programme to promote covitality among Chinese primary school students and found that children who participated in the programme showed significant increases in covitality and sense of school belonging than those who did not. In addition, adopting a whole-school approach to enhance covitality may be promising. For example, a K-12 school in Australia integrated positive psychological traits such as gratitude, optimism, zest, and persistence into its existing school system in various facets including curricula, sports, and student counselling (White & Waters, 2015). By creating an enabling school culture, students were exposed to a school environment where positive psychosocial traits were valued and fostered (White & Waters, 2015).
In comparing the four positive traits, the lowest score was identified for zest, similar to previous empirical studies (Arslan, 2019a; Furlong et al., 2013; Kim et al., 2019; Renshaw, 2017; Wang et al., 2018; Wilkins et al., 2015). Higher scores were found on optimism and persistence, consistent with the findings from Renshaw (2017) and Wang et al. (2018). Given the potential of targeting positive psychological traits to promote flourishing (see discussion in the above subsection), future intervention and development programmes may also consider focusing on zest (i.e., the lowest score across cultures), in addition to the more well-established positive psychological traits such as gratitude (e.g., Kong et al., 2021) and optimism (Miranda & Cruz, 2020).
7.4 Limitations
Firstly, one limitation of the current study was concerned with its representativeness without random sampling, which potentially limits the generalisability of the findings to the larger population of Hong Kong primary school students. Without random sampling, particular sub-groups of students may be over-represented or under-represented (e.g., only 7.0% from Hong Kong Island in the current sample). Secondly, given that our data is cross-sectional, the directionality of the associations could not be ascertained. Previous studies have shown that covitality constructs can be predictors of flourishing (e.g., Furlong et al., 2017; Pennell et al., 2015). The mediational model in the current study was built upon previous empirical evidence, though the results were preliminary in establishing possible pathways from student covitality to flourishing. There is a possibility that the relationships between student covitality, resilience, prosocial behaviour, and flourishing may be bidirectional and mutually reinforcing. For example, student covitality may lead to the development of resilience and prosocial behaviour, which in turn promotes flourishing, and this increased flourishing further strengthens student covitality (e.g., a potential feedback loop; see Fig. 3). Empirical support for such bidirectional relationships could be drawn from research showing that human well-being fosters and is fostered by psychological capitals (Keyes, 2007). Thirdly, we examined the theory-driven model of student covitality (Furlong et al., 2013; Renshaw, 2017) predicting flourishing through resilience and prosocial behaviour, but there could be alternative models of the interrelations among these variables that explain the data equally well. Building on the partial mediating effects reported here, future studies could investigate other school-related predictors and mediators of children’s flourishing. For instance, Datu and Valdez (2019) found that school belongingness mediated the relationships between psychological capitals (e.g., hope and optimism) and life satisfaction among adolescents. Besides, other potential interpersonal factors such as teacher-student relationships (Howells, 2014) and peer relationships (Mitic et al., 2021) may be considered. Importantly, future studies should consider a longitudinal design that elucidates the directionality of the interrelations.
8 Conclusion
Our findings underline the significance of school experience-grounded positive psychological traits by demonstrating its potential predictive effect beyond school-related outcomes, such as school connectedness (Renshaw, 2017), school engagement (Wilkins et al., 2015), and school functioning and adjustment (Arslan & Allen, 2020), in the broad and overarching well-being construct of flourishing. Student covitality as an integrative construct entailing the co-occurrence of the four positive psychological traits can better explain flourishing through both personal (i.e., resilience) and social (i.e., prosocial behaviour) pathways. Our findings shed light on future investigations of drivers of children’s flourishing to consider student covitality as a pivotal variable of resilience-focused (e.g., Dray et al., 2017) or prosociality-focused (e.g., Ellis et al., 2016) intervention and developmental programmes for children. Educators and well-being practitioners may explore further school-based approaches targeting student covitality to nurture student flourishing, including formal and informal school curricula (e.g., literature course and positive education curriculum; White & Waters, 2015).
Acknowledgements
We thank experts such as Professors Edward Diener, Michael Furlong, Tyler L. Renshaw, Wenjie Duan, Bruce Smith, Kelly Erickson, Cixin Wang and Chunyan Yang for granting permissions for us to use the respective instruments. The data of this study was drawn from the project titled “Academic Emotions, Social-Emotional Health, Self-Regulated Learning and Sense of School Membership: Teacher and Student Perspectives” under the Centre for Religious and Spirituality of Education, The Education University of Hong Kong. We thank the anonymous reviewers and the editors for their invaluable comments and suggestions to earlier versions of the manuscript. The views presented in this paper are personal only and do not necessarily represent those of the UNESCO and EdUHK, and do not commit the respective organisations.
Declarations
Competing Interests
The authors have no relevant financial or non-financial interests to disclose.
Ethics Approval
The study was approved by the Human Research Ethics Committee, The Education University of Hong Kong, Hong Kong SAR, China.
Informed Consent
Informed consent was obtained from all individual participants and their legal guardians included in the study.
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