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• W3044283558 abstract "Abstract Mental health is complex, comprising both mental distress and well-being. This study used latent class analysis to identify common combinations of mental distress and well-being (‘mental health classes’) among schoolchildren aged 8–9 years ( N = 3340). Thirteen items, measuring a range of conduct problems, emotional symptoms, and subjective well-being, were included in the analysis. Four mental health classes were identified: (1) complete mental health ( n = 1895, 57%), (2) vulnerable ( n = 434, 13%), (3) emotional symptoms but content ( n = 606, 18%), and (4) conduct problems but content ( n = 404, 12%). The classes were reliably identified across different datasets, and for males and females. Differential relations with covariates indicated that mental health classes were distinct and externally valid. The results supported the dual-factor model of mental health, suggesting that mental distress and subjective well-being are separate continua. Three of the four possible combinations of high and low distress and subjective well-being posited by the dual-factor model were found using this inductive statistical method. Importantly, our analysis also revealed two ‘symptomatic but content’ groups, differentiated by symptom domain (internalising/externalising). The covariate analyses between mental health classes and sociodemographic factors, prior academic attainment, school connectedness, and peer support, indicated that there are nuanced relations between those variables and particular constellations of mental distress and well-being. As one of the few dual-factor studies to focus on middle childhood, the current study adds important new evidence that contributes to our understanding of the complexities of mental health among schoolchildren." @default.
• W3044283558 created "2020-07-29" @default.
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• W3044283558 date "2020-07-25" @default.
• W3044283558 modified "2023-10-17" @default.
• W3044283558 title "Latent Class Analysis of Mental Health in Middle Childhood: Evidence for the Dual-Factor Model" @default.
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• W3044283558 doi "https://doi.org/10.1007/s12310-020-09384-9" @default.
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