Mental health and psychosocial functioning in adolescence: An investigation among Indian students from Delhi

Journal of Adolescence 39 (2015) 59e69

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Mental health and psychosocial functioning in adolescence: An investigation among Indian students from Delhi Kamlesh Singh a, *, Marta Bassi b, Mohita Junnarkar a, Luca Negri c a

Indian Institute of Technology Delhi, India Department of Biomedical and Clinical Sciences Luigi Sacco, University of Milano, Italy c Department of Pathophysiology and Transplantation, University of Milano, Italy b

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 10 January 2015

While developmental studies predominantly investigated adolescents' mental illness and psychosocial maladjustment, the present research focused on positive mental health of Indian adolescents within the Mental Health Continuum model. Aims were to estimate their prevalence of mental health and to examine its associations with mental distress and psychosocial functioning, taking into account age and gender. A group of 539 students (age 13e18; 43.2% girls) in the National Capital Territory of Delhi completed Mental Health Continuum Short Form, Depression Anxiety and Stress Scales-21, Strengths and Difficulties Questionnaire. Findings showed that 46.4% participants were flourishing, 51.2% were moderately mentally healthy, and only 2.4% were languishing. A higher number of girls and younger adolescents were flourishing compared to boys and older adolescents. Moreover, flourishing youths reported lower prevalence of depression and adjustment difficulties, and more prosocial behavior. Findings support the need to expand current knowledge on positive mental health for well-being promotion in adolescence. © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Keywords: Adolescence Mental health Anxiety Depression Stress Psychosocial functioning

Introduction Adolescence is a crucial period in life marking the transition from childhood to adult age. Around 18% of the world's population are in this life stage (United Nations Children's Fund [UNICEF], 2012), characterized by physical, cognitive, and socio-emotional changes which are often perceived as stressful (Frydenberg, 1997), and having potential negative consequences in terms of psychosocial adjustment. Deviant behavior, poor scholastic achievement, drug abuse and social maladjustment are among the most frequent problems detected in adolescence, having far-reaching implications for adult development (Cicchetti & Cohen, 2006). Many studies have found associations between these problems and youth's mental health (UNICEF, 2011). There is thus great concern among nations worldwide about the incidence of mental illness in adolescence: It is estimated that around 20% of the world's youngsters have a mental health problem, with anxiety disorders and depression largely contributing to the global burden of disease for people aged 12e18 (Costello, Egger, & Angold, 2005). According to the International Consortium in Psychiatric Epidemiology (ICPE, 2000), the median age of the first onset of any anxiety, substance and mood disorders is

* Corresponding author. Dept. of Humanities and Social Sciences, Indian Institute of Technology Delhi, India. Tel.: þ91 9968363625. E-mail addresses: [email protected], [email protected] (K. Singh). http://dx.doi.org/10.1016/j.adolescence.2014.12.008 0140-1971/© 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

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between 15 and 26 across several countries in Europe, North America, and Latin America. High levels of stress are also detected in this life stage, which in turn are associated with psychopathology (Grant et al., 2006). In addition, variability in mental illness severity was found among youths in relation to age and gender. Overall, compared to late adolescence (age 15e19), early adolescence (age 12e14) is characterized by substantive developmental transitions (Petersen, Kennedy, & Sullivan, 1991), such as change from elementary to middle school, different peer expectations, and novel roles and relationships within the family and other life contexts. These transitions account for higher mean levels of stress, anxiety and depression among early than late adolescents. Moreover, irrespective of age, girls generally fare worse than boys (Hale, Raaijmakers, Muris, van Hoof, & Meeus, 2008; Holsen, Kraft, & Vittersø, 2000; Seiffge-Krenke, Aunola, & Nurmi, 2009). While mental illness clearly represents a world's challenge at the heart of World Health Organization (WHO)'s Mental Health Action Plan 2013e2020, researchers in positive psychology have long called for a shift in focus from the sole investigation and repair of human shortcomings, deficits and pathologies, to the construction and implementation of individual strengths, resources, and ultimately positive mental health (Seligman & Csikszentmihalyi, 2000). Keyes (2002, 2007) has argued that most research has equated mental health with absence of psychopathology, neglecting WHO's positive definition of mental health as “a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community” (2004, p. 12). In line with this definition, Keyes proposed a measure of positive mental health hinging on two longstanding traditions in positive psychology: The hedonic tradition dealing with feelings of happiness, satisfaction and positive emotions; and the eudaimonic tradition focusing on optimal functioning in individual and social life (Deci & Ryan, 2008; Keyes, 1998). Mental health comprises emotional well-being e the affect component of hedonia e as well as psychological and social well-being, reflecting how well individuals perceive themselves as functioning in life according to the eudaimonic tradition. Psychological well-being comprises self-acceptance, personal growth, purpose in life, positive relations with others, autonomy, and environmental mastery (Ryff, 1989), while social well-being consists of social integration, contribution, coherence, actualization, and acceptance (Keyes, 1998). According to the Mental Health Continuum model (Keyes, 2005, 2007), mental health e like mental illness e is a syndrome of symptoms. Keyes uses the term diagnosis to identify mental health categories, in order to parallel the Diagnostic and Statistical Manual of Mental Disorders (DSMeIIIeR; American Psychiatric Association, 1987) approach to appraising mental illness. Consequently, a diagnosis of mental health is made when an individual exhibits high levels on at least one symptom of hedonia and on just over half of the symptoms of eudaimonia. Under this condition, individuals are diagnosed as flourishing in life. When individuals report low levels on at least one symptom of hedonia and on just over half of the symptoms of eudaimonia, they are diagnosed as languishing. A diagnosis of moderate mental health is done when individuals are neither flourishing nor languishing. Accordingly, studies targeting adults in countries such as Italy, South Africa and the Netherlands showed that the majority of participants were categorized as non-flourishing, namely languishing or moderately mentally healthy (between 63% and 80%; Keyes et al., 2008; Petrillo, Capone, Caso, & Keyes, 2014; Westerhof & Keyes, 2010). In addition, initial evidence was gathered showing that mental health varies based on sociodemographic variables, such as education, employment status, age, and gender. Higher education and having a job positively correlated with mental health in US and South African samples (Keyes, 2002; Khumalo, Temane, & Wissing, 2012). Regarding gender, men in the US sample reported better mental health than women (Keyes, 2002), but this finding has not yet been replicated in other studies. Data concerning age suggest that, within the range of 25e74 years, mental health is higher among adults between 65 and 74 (Keyes, 2002). However, longitudinal data revealed that mental health is dynamic over time, with single adult individuals moving up or downward the mental health continuum as age advances (Keyes, Satvinder, Dhingra, & Simoes, 2010). Confirmatory factor analysis substantiated the tripartite structure of mental health across countries, and further corroborated that mental health and mental illness are not opposite ends of a continuum (Joshanloo, Wissing, Khumalo, & Lamers, 2013; Keyes et al., 2008; Lamers, Westerhof, Bohlmeijer, Klooster, & Keyes, 2011; Petrillo et al., 2014; Westerhof & Keyes, 2010). Findings highlighted independent effects of mental health on a number of indicators of psychosocial functioning. In particular, languishing was shown to have similar negative effects as depression. For instance, both were associated with low perceived emotional health, limitations in activities of daily living, and days lost or cutback from work (Keyes, 2002). By contrast, flourishing was associated with better psychosocial functioning. Recently, longitudinal data also showed that changes in level of positive mental health were predictive of future risk of mental illness, with gains in mental health decreasing the odds of mental illness incidence (Keyes et al., 2010). In addition, absence of positive mental health was shown to increase the probability of all-cause mortality for men and women at all ages after adjustment for known causes of death (Keyes & Simoes, 2012). In spite of the promising results obtained among adults about the positive consequences associated with mental health, to date few studies have targeted adolescence. In one US research (Keyes, 2006), flourishing youths aged 12e18 amounted to 37.9%, languishing to 6.2%, and moderately mentally healthy to 55.9%. Similarly, South African adolescents aged 15e17 were shown to be flourishing with a prevalence of 42%, to be languishing with 5%, and to be moderately mentally healthy with 53% (van Schalkwyk & Wissing, 2010). In addition, the US findings detected a decline in mental health from early to late adolescence. Specifically, nearly 10% more youths in the age group 12e14 were flourishing compared to youths in the range 15e18; by contrast, nearly 10% more older adolescents were moderately mentally healthy than younger adolescents. Notably, however, US findings supported results obtained with adult samples revealing that, irrespective of age, flourishing youths functioned better than those with moderate mental health, who in turn functioned better than languishing youths. In particular, the flourishing reported the fewest depressive symptoms and the fewest conduct problems such as having been

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arrested, skipped school, alcohol use, cigarette smoking, and marijuana use. They also reported the highest levels of selfconcept, self-determination, closeness to other people and school integration. Taken together, all these findings cast a new light on adolescence, stressing not only the need to analyze youths' risks of developing maladjustment and psychopathology, but also to explore youths' potentials and resources for thriving in life. More studies are needed to better understand mental health from a developmental perspective and also to extend the study of youths' positive mental health to other countries. These would broaden our understanding of the mental health continuum model and its implications for promoting optimal functioning in adolescence. For this reason, the present study was performed to investigate positive mental health among Indian adolescents. Indian youths and mental illness India is the country with the largest share of the world's adolescent population (20.5%), amounting to around 243 million youths (UNICEF, 2011). Adolescents represent an important resource for the Indian people as witnessed in the ancient text of Dharamashastra, which recognized the nature of adolescence and prescribed specific codes of conduct for this phase of life which even today continue to influence the cultural practices towards adolescents (Verma & Saraswathi, 2002). In particular, youngsters are expected to conform and be loyal to family norms and group harmony, in line with Indian collectivistic cultural values (Carlo, Fabes, Laible, & Kupanoff, 1999). Like in the other countries in the world, there is growing concern in India about the mental health status of its youth (WHO, 2005). National figures are substantially absent for various reasons, including the difficulty of reaching teens living in rural areas or those not attending schools, as well as the use of screening tools which are often unsuitable for assessing youthspecific disorders (Bhola & Kapur, 2003). However, a number of studies using standardized instruments have reported prevalence of psychiatric morbidity in the range between 14.4% and 31.7% (WHO, 2005). Higher morbidity is associated with male gender, low socioeconomic status (SES), and poor parental education. More specifically, among urban adolescents studying in private schools in Chennai (South India), it was observed that 37.1% were mildly, 19.4% were moderately, and 4.3% were severely depressed (Mohanraj & Subbaiah, 2010). While no significant gender differences were detected, a higher percentage of older adolescents were found to be mildly depressed than younger adolescents. By contrast, among students aged 13e18 in Great Noida (a township within the National Capital Region; Bhasin, Sharma, & Saini, 2010), girls reported higher levels of depression than boys, but similar scores in anxiety and stress. In addition, depression and stress were found to be significantly associated with adverse events over the course of time, poor psychosocial functioning such as poor academic performance (Bhasin et al., 2010), as well as suicidal behavior (Sanjeev, Sharma, Kabra, Shalini, & Dogra, 2004). Mishra and Sharma (2001) further demonstrated that perceived relationship with father, mother's love and one's appearance were associated with depression and anxiety among girls aged 12e18 going to school in New Delhi. In general, girls tended to report higher adjustment difficulties than boys, but also more prosocial behavior, as witnessed in a sample of students aged 11e16 going to urban schools in Bangalore (South India; Bharat Kumar Reddy, Biswas, & Rao, 2011). Study aims To the best of our knowledge, no study has thus far targeted positive mental health among Indian adolescents. In an initial attempt to fill in the void in empirical literature, we performed an exploratory study among school-going adolescents from metropolitan areas in the National Capital Territory of Delhi (NCT-Delhi). Aims of the study were to (1) estimate the prevalence of mental health among participants; (2) examine the incidence of mental distress in mental health categories; and (3) explore the associations of mental health with psychosocial functioning. Adopting a developmental perspective, all examinations took into account adolescents' gender and age. Methods Participants and school setting A total of 539 adolescents participated in the study. Of them, 56.8% (N ¼ 306) were boys and 43.2% (N ¼ 233) were girls. Their age ranged between 13 and 18 years (M ¼ 15 years, SD ¼ 1.4): 35.1% of the participants were in the range 13e14 years and 64.9% in the range 15e18 years. They resided in nuclear families (62.2%) and joint families (35.2%); few did not provide this information (2.6%). Participants were enrolled in class 8 (26.5%), class 9 (7.4%), class 10 (18.2%), class 11 (40.6%), and class 12 (7.2%) of private schools located in urban areas of NTC-Delhi. According to the National University of Educational Planning and Administration (2012e2013), about half of the schools in this territory are private (47.1%), reflecting national figures. Compared to state-run government schools, private ones are run by non-government organizations such as corporate houses and trusts. Private school enrollment augmented from 16.3% in 2005 to about 22.6% in 2008 e an increase of about 40% (Wadhwa, 2009). Such a dramatic boost is related to parents' belief that private schools provide a better quality education. While this issue is still a matter of national debate in terms of learning advantages, tangible assets are lower pupil-teacher ratio, higher teacher attendance, and supply of tuition in English leading to better job prospects in the future (ASER, 2009). Attendance in these schools is not limited to the wealthy or to children living in urban areas. A large number of children belonging to disadvantaged households study in private schools which charge low fees, and nearly 30% of villages in

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India have access to a private school within the village itself (Goyal & Pandey, 2009). Moreover, attendance was recently supported by the 2010 educational policy of the Indian Government providing facilitated access to students from lower income groups. In the present study, we collected self-report information on participants' family income. However, it could not be held reliable because, culturally, it is not customary for parents to share income details with their children, and some students were likely to have misunderstood the question. Instruments Mental Health Continuum Short Form The Mental Health Continuum Short Form (MHC-SF; Keyes et al., 2008) measures mental health on 6-point scales ranging from 0 (‘never’) to 5 (‘everyday’). It comprises 14 items, of which 3 measure the frequency of emotional well-being (EWB), 6 psychological well-being (PWB), and 5 social well-being (SWB). Sample items are ‘During the past month, how often did you feel’: ‘happy’ (EWB), ‘that you had experiences that challenged you to grow and become a better person’ (PWB), ‘that you had something important to contribute to society’ (SWB). The items for each dimension were summed to their total score, with higher ratings indicating superior levels of well-being. Internal consistency was good: a ¼ .82 for EWB; a ¼ .84 for PWB; a ¼ .79 for SWB. Depression Anxiety Stress Scales 21 The Depression Anxiety Stress Scales-21 (DASS-21; Lovibond & Lovibond, 1995) is the widely used short version of a 42item self-report instrument designed to measure distress along the axes of depression, anxiety and tension/stress. Each dimension is measured with 7 items on 4-point rating scales ranging from 0 (‘did not apply to me at all’) to 3 (‘applied to me very much, or most of the time’). Sample items are: ‘I couldn't seem to experience any positive feeling at all’ (depression), ‘I felt I was close to panic’ (anxiety), and ‘I found it hard to wind down’ (stress). According to DASS-21 guidelines (Crawford & Henry, 2003; Lovibond & Lovibond, 1995), in order to have equivalent scores to the full-length DASS, the total score of each scale was multiplied by two and ranged from 0 to 42. In this study, reliability indices were good: a ¼ .81 for stress, a ¼ .80 for anxiety, and a ¼ .84 for depression. Strengths and Difficulties Questionnaire The Strengths and Difficulties Questionnaire (SDQ; Goodman, 1997) is a brief 25-item instrument developed to assess youths' psychosocial adjustment. Items are measured on 3-point scales ranging from 0 (‘not true’) to 2 (‘certainly true’). Five scales with 5 items each evaluate emotional symptoms (‘I worry a lot’), conduct problems (‘I am often accused of lying or cheating’), hyperactivity (‘I am restless. I cannot stay still for long’), peer problems (‘Other children or young people pick on me’), and prosocial behavior (‘I often volunteer to help others’). The sum of the items in each subscale gave their total scores, ranging from 0 to 10. In line with previous studies (Hawes & Dadds, 2004; van Widenfelt, Goedhart, Treffers, & Goodman, 2003), the scales for emotional symptoms, conduct problems, hyperactivity and peer problems had rather low alphas (ranging from .40 to .67). As suggested in the literature (Achenbach et al., 2008), we thus generated a total adjustment difficulties score summing up the scores for these subscales (score range: 0e40; a ¼ .76). Alpha for the prosocial behavior scale amounted to .67. Procedure This study was a part of a larger project conducted to explore the well-being of adolescents in metropolitan areas of the National Capital Region (NCR). The NCR includes NTC-Delhi and adjoining urban areas, accounting for India's largest agglomeration with over 54 million people. For the present study, the researchers randomly contacted authorities of 12 private schools in NTC-Delhi to obtain permission for data collection. Five of these schools (41.7%) accepted to participate. After principals' consent and permission, teachers were requested to arrange a 1-h session during their classes so that researchers could hand out questionnaire booklets measuring the variables of interest. Questionnaires were in English. Participants were briefed about the nature of the study and consent was taken. Further, instructions were given for filling up the booklet, and students' timely doubts were clarified. Students were assured of confidentiality of information. Data analysis Collected data were pooled together disregarding participants' specific school affiliation. Since this was the first time that MHC-SF had been administered to Indian participants, we preliminary tested its factor structure using a split-sample approach. The full sample was randomly split into two, keeping the same percentages for gender and age in each group as in the whole sample. One half (Sample 1; N ¼ 269) was employed to investigate the factorial structure of MHC-SF by exploratory factor analysis. Data violated the assumption of multivariate normality, as resulted from Mardia's multivariate omnibus test; we thus opted for a principal axis factoring analysis (Fabrigar, Wegener, MacCallum, & Strahan, 1999). Oblimin direct rotation method was used to allow factors to correlate, and KaisereGuttman's criterion (Kaiser, 1960) and Cattell's scree test (Cattell, 1966) were employed to identify the final number of factors. Subsequently, we performed confirmatory factor analysis on the other half of the sample (Sample 2; N ¼ 270). Again, data violated the assumption of multivariate normality. Therefore, SatorraeBentler scaled correction of Maximum Likelihood

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estimation method was used as it provides a more robust fit measure for non-normal distributions (Satorra & Bentler, 1994). Several indices were taken into account to evaluate the model goodness of fit: SatorraeBentler Scaled Chi-Square (SeB c2), Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), Standard Root Mean Square Residual (SMSR), Goodness of Fit Index (GFI) and Adjusted Goodness of Fit Index (AGFI). Hu and Bentler (1995, 1999) suggested the following cut-off criteria for model fit acceptability: RMSEA < .06, CFI > .95, SRMR < .08, GFI and AGFI > .90. After MHC-SF structure analysis, we computed mental health categorical diagnosis based on Keyes (2005; Keyes et al., 2008). Adolescents were diagnosed with flourishing mental health if they experienced at least 1 of the 3 symptoms of hedonic well-being ‘every day’ or ‘almost every day’ and at least 6 of the 11 positive functioning symptoms ‘every day’ or ‘almost every day’ in the past month. Adolescents were diagnosed with languishing mental health if they experienced at least 1 of the 3 symptoms of hedonic well-being ‘never’ or ‘once or twice’, and reported 6 of the 11 positive functioning symptoms ‘never’ or ‘once or twice’ during the past month. Youths who were neither languishing nor flourishing were diagnosed with moderate mental health. Concerning mental illness, DASS-21 is intended as a quantitative measure of distress and not as a categorical measure of clinical diagnoses (Osman et al., 2012). However, for clinical purposes, severity categories can be identified based on normative English-speaking reference populations for the full 42-item DASS scores (Lovibond & Lovibond, 1995). Five categories are obtained for normal, mild, moderate, severe, and extremely severe distress levels. On the basis of these categories, we adopted a more stringent categorization criterion, identifying three groups for depression, anxiety and stress, respectively: normal, moderate (collapsing mild and moderate scores), and severe (including severe and extremely severe scores). For depression, normal ratings ranged from 0 to 9, moderate ratings from 10 to 20, and severe ratings from 21 to 42. For anxiety, normal ratings ranged from 0 to 7, moderate ratings from 8 to 14, and severe ratings from 15 to 42. For stress, normal ratings ranged from 0 to 14, moderate ratings from 15 to 25, and severe ratings from 26 to 42. To meet our study aims, we further computed descriptive statistics of and correlation analyses between measures of mental health, mental distress, and psychosocial functioning with age and gender. Age was dummy coded with 0 for younger adolescents aged 13e14 years, and 1 for older adolescents aged 15e18; gender was coded 0 for girls and 1 for boys. A series of hierarchical loglinear analyses and post-hoc chi-square tests were then performed to investigate the association among agegroup, gender, categorical diagnosis of mental health and mental distress. Three-way ANOVAs were finally calculated to compare levels of psychosocial functioning (in terms of total adjustment difficulties and prosocial behavior) across younger and older boys and girls with different diagnosis of mental health. Results MHC-SF structure Prior to exploratory factor analysis (EFA), we used the KaisereMeyereOlkin (KMO) measure to verify sampling adequacy for the principal axis factoring analysis conducted on the 14 MHC-SF items in Sample 1. Total KMO amounted to .91, and all KMO values for individual items were higher than .85, that is above the acceptable limit of .50 (Hutcheson & Sofroniou, 1999). Bartlett's test of sphericity (c2(91) ¼ 1750.69, p < .001) indicated that correlations between items were sufficiently large to perform EFA. Three factors showed eigenvalues above KaisereGuttman's criterion of 1, together explaining 53.6% of the variance. Scree plot's inflexion point further confirmed the adequacy of this solution. Table 1 shows MHC-SF factor loadings after rotation. All MHC-SF items' largest loadings were on the expected factor. Both items 4 and 5 showed relatively low loadings (respectively .30 and. 39) on social well-being, in line with results from Lamers et al. (2011) on the Dutch version of MHC-SF.

Table 1 Oblimin-rotated MHC-SF factor loadings in sample 1 (N ¼ 269). MHC-SF items

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.

Principal axis factoring Emotional well-being

Social well-being

Psychological well-being

.65 .90 .70 .21 .11 .03 .07 .08 .07 .04 .10 .06 .01 .16

.05 .01 .01 .30 .39 .88 .67 .79 .16 .01 .17 .16 .04 .03

.09 .08 .12 .08 .16 .06 .05 .02 .57 .83 .77 .56 .62 .64

Note. Bold ¼ item highest factor loading.

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All the remaining item loadings were higher than .56 on the expected factor, and lower than .22 on the other factors, thus confirming the adequacy of the original model (Keyes et al., 2008). The factor correlation between emotional and psychological well-being was highest (.73), followed by the correlation between psychological and social well-being (.58), and between emotional and social well-being (.45). To further test the adequacy of this model, we performed confirmatory factor analysis (CFA) on MHC-SF items in Sample 2. The goodness of fit of the three-factor solution was more than satisfactory, and similar to results from previous studies (Keyes et al., 2008; Lamers et al., 2011): SeB c2(74) ¼ 130.48, p < .001; RMSEA ¼ .053, 90% CI ¼ .038e.068; CFI ¼ .98; SMSR ¼ .058; GFI ¼ .91; AGFI ¼ .88. The CFA model is depicted in Fig. 1. The standardized values of loading estimates ranged from .44 (item 4) to .86 (item 2) and were statistically significant (p < .001). As in Sample 1, for Sample 2 emotional and psychological well-being showed the highest correlation value (.70), followed by the correlation between psychological and social well-being (.65), and between emotional and social well-being (.54). Altogether, results from exploratory and confirmatory analyses fully supported the adequacy of the tripartite structure of MHC-SF. Mental health and distress among Indian adolescents Table 2 summarizes the descriptive statistics and correlations obtained for this study. Notably, weak negative correlations between mental health and mental distress were detected. In particular, correlations of emotional and psychological wellbeing with stress, anxiety, and depression ranged between r ¼ .22 and r ¼ .10 (ps < .05), while no significant scores were observed between social well-being and distress. In addition, modest correlations were detected between age and mental health and distress: Compared to younger adolescents, older ones reported lower emotional, social and psychological well-being, as well as lower levels of stress, anxiety, and depression. As for gender, boys reported lower psychological wellbeing than girls. The categorical diagnosis of mental health revealed that 46.4% of the participants were flourishing (N ¼ 250), 51.2% reported moderate mental health (N ¼ 276), and 2.4% were languishing (N ¼ 13). Since the languishing category was extremely infrequent, it was collapsed with the moderately mentally healthy category for further analyses, as suggested in Westerhof

Fig. 1. MHC-SF three-factor model in Sample 2 (N ¼ 270).

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Table 2 Descriptive statistics and correlations among measures of mental health (MHC-SF), mental distress (DASS-21) and psychosocial functioning (SDQ). Variables

M

SD

Range

1.

2.

1. Agea 2. Genderb 3. EWB 4. SWB 5. PWB 6. TWB 7. Stressc 8. Anxietyc 9. Depressionc 10. TAD 11. Prosocial behavior

.64 .57 10.89 13.93 21.54 46.35 14.10 11.81 12.39 14.25 7.64

.48 .50 3.09 5.54 5.74 12.15 9.14 9.18 9.89 5.89 2.03

0e1 0e1 0e15 0e25 1e30 6e70 0e42 0e42 0e42 1e30 0e10

e .01 .12** .21*** .09* .17*** .21*** .23*** .24*** .15*** .16***

3.

e .02 .02 .11* .07 .02 .04 .07 .01 .20***

4.

5.

6.

7.

8.

9.

10.

e .48*** .62*** .77*** .15** .11** .22*** .31*** .22***

e .57*** .85*** .01 .06 .00 .11** .22***

e .89*** .15*** .10* .22*** .34*** .34***

e .10** .05 .16*** .29*** .32***

e .79*** .80*** .45*** .14***

e .78*** .45*** .15***

e .46*** .22***

e .31***

Note: EWB ¼ emotional well-being; SWB ¼ social well-being; PWB ¼ psychological well-being; TWB ¼ total well-being; TAD ¼ total adjustment difficulties. *p < .05; **p < .01; ***p < .001. a Age was coded 0 for younger adolescents aged 13e14 years, and 1 for older adolescents aged 15e18. b Gender was coded 0 per girls and 1 for boys. c Comparison with normative data showed that, on average, anxiety and depression were in the moderate range and stress in the normal range, in line with results from a sample of adolescents living in the National Capital Region (Bhasin et al., 2010).

and Keyes (2010). We thus created two groups, one including flourishing participants (N ¼ 250; coded 1) and one comprising non-flourishing youths (N ¼ 289; coded 0). As for the prevalence of mental distress, Table 3 illustrates the percentage distributions of stress, anxiety and depression by mental health diagnosis and, globally, for the whole sample. At the sample level, severe ratings were reported by 12.8% participants for stress, 34.3% for anxiety and 21.7% for depression. Independent four-way loglinear analyses (age group  gender  mental health category  each of DASS distress axis) revealed no significant four-way effects; no interactions between age and gender were observed at any level of analyses. Three-way loglinear analysis concerning age group  gender  mental health produced a final model (c2(2) ¼ 4.58, p ¼ .10) that only retained interactions of mental health with gender and age. Inspection of the adjusted standardized residuals of post-hoc chi-square tests showed that a higher number of girls compared to boys (c2(1) ¼ 10.89, p < .001) and a higher number of younger compared to older adolescents (c2(1) ¼ 14.92, p < .001) fell into the flourishing group. Loglinear analyses concerning age group  gender  mental distress produced final models that retained the interactions of age group with stress (c2(5) ¼ 4.38, p ¼ .50), anxiety (c2(5) ¼ 3.75, p ¼ .59), and depression (c2(5) ¼ 8.63, p ¼ .125). For all these variables (stress: c2(2) ¼ 18.59, p < .001; anxiety: c2(2) ¼ 26.36, p < .001; depression: c2(2) ¼ 26.70 p < .001), a lower number of younger participants reported normal ratings and a higher number reported severe ratings compared to older youths. Finally, we tested independent three-way models with age group  mental health  distress, and gender  mental health  distress. No significant three-way interactions were detected. Besides the age and gender differences in mental health and distress described above, a significant association between mental health and depression was found (c2(2) ¼ 14.33, p < .001). Examination of the adjusted standardized residuals showed that a higher number of flourishing participants fell into the normal range of depression and a lower number of them fell into the moderate range compared to non-flourishing individuals. Mental health and psychosocial functioning In our last analyses, we investigated the association of adolescents' mental health with psychosocial functioning taking into account age and gender. Table 4 illustrates the mean levels of adjustment difficulties and strengths by mental health category (flourishing vs non-flourishing). Three-way ANOVAs highlighted significant models for both adjustment difficulties and prosocial behavior. Concerning adjustment difficulties, significant independent effects were found for age (F(1, 531) ¼ 18.97, p < .001; partial h2 ¼ .034) and mental health (F(1, 531) ¼ 33.62, p < .001; partial h2 ¼ .06). Accordingly, younger adolescents reported higher difficulty scores than older ones (respectively, M ¼ 15.46, SD ¼ 6.13 and M ¼ 13.59, SD ¼ 5.66), and the flourishing reported lower ratings than the non-flourishing. Regarding prosocial behavior, significant effects were detected for age (F(1, 531) ¼ 27.23, p < .001; partial Table 3 Percentage distribution of mental distress among flourishing and non-flourishing adolescents. Stress levels

Non-flourishinga (N ¼ 289) % Flourishing (N ¼ 250) % Total sample (N ¼ 539) %

Anxiety levels

Depression levels

Normal

Moderate

Severe

Normal

Moderate

Severe

Normal

Moderate

Severe

56.4 60.0 58.1

30.1 28.0 29.1

13.5 12.0 12.8

34.6 42.0 38.0

29.4 25.6 27.6

36.0 32.4 34.3

38.1 54.0 45.5

38.4 26.4 32.8

23.5 19.6 21.7

a The categories “languishing” and “moderate mental health” were collapsed into a single category “non-flourishing” because of the low number of languishing participants (N ¼ 13).

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Table 4 Adjustment difficulties and strengths among flourishing and non-flourishing adolescents. Total adjustment difficulties

Non-flourishinga (N ¼ 289) Flourishing (N ¼ 250)

Prosocial behavior

M

SD

M

SD

15.46 12.84

5.57 5.96

7.14 8.22

2.13 1.75

a The categories “languishing” and “moderate mental health” were collapsed into a single category “non-flourishing” because of the low number of languishing participants (N ¼ 13).

h2 ¼ .049), gender (F(1, 531) ¼ 8.91, p < .01; partial h2 ¼ .02), mental health (F(1, 531) ¼ 50.58, p < .001; partial h2 ¼ .09), and the interaction between age and mental health (F(1, 531) ¼ 6.01, p < .02; partial h2 ¼ .01). In line with previous studies (Luengo Kanacri et al., 2014), younger adolescents were less prosocial than older ones (M ¼ 7.19, SD ¼ 2.18 and M ¼ 7.89, SD ¼ 1.90), and girls were more prosocial than boys (M ¼ 8.11, SD ¼ 1.72 and M ¼ 7.29, SD ¼ 2.17). As for mental health, flourishing adolescents were more prosocial than non-flourishing youths. In particular, inspection of the interaction plot between age and mental health (Fig. 2) showed that being flourishing compared to non-flourishing was especially valuable for younger than for older adolescents in terms of reported prosocial behavior. Discussion The current research applied the Mental Health Continuum model (Keyes, 2002) in the investigation of Indian adolescents' mental health. As a preliminary finding, the analysis of the psychometric properties of the model assessed through MHC-SF gave support to the tripartite factor structure of mental health. In line with previous studies (Joshanloo et al., 2013; Keyes et al., 2008; Lamers et al., 2011; Petrillo et al., 2014), exploratory and confirmatory analyses among Indian adolescents supported the adequacy of the model as constituted of emotional, psychological and social well-being. Aligning with these studies, findings further revealed few weak negative correlations between mental health and distress, showing that these are not opposite ends of a single measurement continuum. Implications are that absence of mental illness does not entail presence of mental health and, conversely, presence of mental illness does not imply absence of mental health (Keyes & Cartwright, 2013). Different combinations can be detected between categories of mental health and mental illness: Only a complementary analysis of the two can provide exhaustive information on individuals' overall conditions. Consequently, we investigated the prevalence of the mental health diagnoses among participants. Findings showed that youths were almost equally distributed between the categories of moderate mental health and flourishing (respectively, 51.2%

Fig. 2. Interaction effect between age group and mental health (flourishing vs non-flourishing).

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and 46.4%), while only as few as 2.4% were languishing. Interpreting these figures in light of international adolescent studies, we observed that fewer Indians were languishing: About three times as many US youths (Keyes, 2006)1 and twice as many South African youngsters (van Schalkwyk & Wissing, 2010) were mentally unhealthy. In addition, slightly more Indians were flourishing compared to the other groups. Cultural and contextual reasons could be brought forward to explain these differences, such as supportive social environment or strong family ties which, according to Hofstede (2001), characterize predominantly collectivistic countries such as India, and to a much lower extent South Africa or the US. At the same time, our participants were not representative of the entire Indian youth population: They were urban adolescents living in NCT-Delhi, attended private schools, and were proficient in English. These characteristics e along with possible upper SES of families e may have contributed to their level of mental health such that future studies are needed to delve deeper into the contextual and personal factors favoring mental health within and across countries. Future studies should also take into account pupils' specific school affiliation in order to control for possible clustering effects in data analysis. In addition, by collecting data from a higher number of schools, more sophisticated statistical analyses (e.g. multilevel modeling) could be performed, taking into account specific school-related characteristics which may foster or hamper pupils' mental health. Nonetheless, factors such as age and gender were taken into consideration in this research. Like in the study among US youths (Keyes, 2006), present findings revealed that a higher number of younger adolescents were diagnosed as flourishing as compared to older youths. In addition, we found that a higher number of girls relative to boys fell into the flourishing group. From the developmental perspective, it is interesting to consider these findings in the face of those regarding mental distress. Concerning measures of mental distress, caution is warranted as DASS-21 does not reflect clinical diagnoses. The questionnaire was however validated against individual psychiatrist-administered Structured Clinical Interviews for DSM IV Axis 1 Diagnoses (SCID) modules for depression and anxiety (Lovibond & Lovibond, 1995; Tran, Tran, & Fisher, 2013). Our findings detected a significant relation between distress and age e but not gender e with younger adolescents experiencing more severe symptoms of anxiety, stress and depression than older ones (Hale et al., 2008; Holsen et al., 2000; Seiffge-Krenke et al., 2009). Along with findings about mental health, these results suggest a dynamic change over time (Keyes & Cartwright, 2013), according to which both mental health and mental distress decrease as age advances. Previous studies however showed that some individuals report stable scores of positive mental health and distress over time, whereas others report highly fluctuating scores, with a combined shift from presence to absence of positive mental health and from presence to absence of mental illness, or vice versa (Hale et al., 2008; Keyes et al., 2010). It would thus be fruitful to conduct longitudinal analyses in the future in order to detect specific trajectories for single individuals. In addition, concerning gender, evidence of the “flourishing advantage” of girls compared to boys speaks for a compensation effect for the frequent higher incidence of depression detected among girls (Bhasin et al., 2010; Hale et al., 2008; Holsen et al., 2000; Seiffge-Krenke et al., 2009). This girls' advantage seems to be pervasive, i.e. unrelated to adolescent age, at least in this Indian sample. From a long-term perspective, previous US studies focusing only on psychological well-being highlighted that women in different age ranges (from 25 to 74 years) not only tended to report higher depression prevalence and stable higher scores of positive social relations than men, but also higher levels of autonomy and environmental mastery as age increased (Ryff & Singer, 2008). Monitoring girls and boys in the transition from adolescence to adulthood may thus cast new light on gender-related mental health trajectories over time. The next aim of our analyses was to explore the implications of mental health in terms of incidence of mental distress among participants. Because of the low number of individuals falling into the languishing category, they were merged into a non-flourishing category along with the more numerous moderately mentally healthy youths. Thus, we could not statistically assess the more extreme effects initially observed for languishing participants at the descriptive level. This could also partly explain why flourishing and non-flourishing adolescents did not differ in stress and anxiety levels. By contrast, irrespective of age and gender, adolescents significantly differed in relation to depression, confirming many previous studies among both adult and adolescent samples (Keyes, 2006; Keyes et al., 2008; Lamers et al., 2011; Petrillo et al., 2014; Westerhof & Keyes, 2010). In particular, compared to the non-flourishing group, a higher number of flourishing adolescents were in the normal range of depression and a lower number was in the moderate range. This finding is quite relevant in light the protective role of mental health in relation to depression, as detected by Keyes et al. (2010) in their longitudinal study among adults. Finally, we addressed the relationship between mental health and psychosocial functioning. SDQ adjustment measures were based on participants' self-reports, thus requiring future support from convergence studies with parents' and teachers' reports (Van Roy, Veenstra, & Clench-Aas, 2008). In addition, due to the low reliability scores obtained for single difficulty factors we could not run more fine-grained analyses in relation to youths' mental health. However, in line with previous research (Keyes, 2006; Keyes et al., 2012), findings showed that not only did flourishing youths report a lower incidence of depression, they also functioned better than non-flourishing participants. In particular, flourishing youths reported lower levels of adjustment difficulties and higher scores of prosocial behavior. Moreover, in spite of the fact that younger adolescents globally were less prosocial than older ones, a significant interaction effect between mental health and age showed that being flourishing was even more beneficial for younger adolescents as it was accompanied by even higher levels of prosocial behavior.

1 Comparison between Indian and US findings should be considered with caution as two MHC-SF items (purpose in life and self-acceptance) were not measured in the US study.

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To conclude, findings from the present study support the need to complement investigation of adolescents' mental illness with analysis of positive mental health. Flourishing in adolescence is associated with desirable outcomes such as low depression, low adjustment difficulties and enhanced prosocial behavior, deserving further attention in view of well-being promotion. This is in line with the great attention devoted to adolescence in Indian society, as expressed in the ancient text of Dharamashastra. In particular, more longitudinal studies are needed to shed light on the factors contributing to adolescents' mental health taking into account developmental challenges associated with age and gender. Our study tentatively suggests that intervention strategies could particularly benefit older adolescents and boys, among whom the flourishing status was less frequent. In this respect, research showed that school-based programs of well-being therapy and positive education (Fava & Ruini, 2014) are effective in favoring positive social, health and behavioral outcomes, with beneficial effects extending to young adulthood (Hawkins et al., 2012). Finally, as cross-cultural evidence is accumulating on adolescence as a critical period for mental illness onset (ICPE, 2000) and on the generalizability of the mental health continuum beyond national borders, further research is needed to understand culture-specific resources available to youth and contributing to a flourishing society. Acknowledgment This research received funding from the Indian Council of Medical Research (ICMR) (RP02586), India on “Relationship of Demographic variables, socio-cultural issues and selected psychological constructs with the positive mental health of north Indian adolescents.” This paper is a part of this project. Authors would like to thank the funding agency for its support. 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