Development of a dual-factor measure of adolescent mental health: an analysis of cross-sectional data from the 2014 Canadian Health Behaviour in School-aged Children (HBSC) study
Bibliographic record
Abstract
INTRODUCTION: Studies of adolescent mental health require valid measures that are supported by evidence-based theories. An established theory is the dual-factor model, which argues that mental health status is only fully understood by incorporating information on both subjective well-being and psychopathology. OBJECTIVES: To develop a novel measure of adolescent mental health based on the dual-factor model and test its construct validity. DESIGN: Cross-sectional analysis of national health survey data. SETTING AND PARTICIPANTS: Nationally weighted sample of 21 993 grade 6-10 students; average age: 14.0 (SD 1.4) years from the 2014 Canadian Health Behaviour in School-aged Children study. MEASURES: Self-report indicators of subjective well-being (life satisfaction, positive and negative affect), and psychopathology (psychological symptoms and overt risk-taking behaviour) were incorporated into the dual-factor measure. Characteristics of adolescents families, specific mental health indicators and measures of academic and social functioning were used in the assessment of construct validity. RESULTS: Proportions of students categorised to the four mental health groups indicated by the dual-factor measure were 67.6% 'mentally healthy', 17.5% 'symptomatic yet content', 5.5% 'asymptomatic yet discontent' and 9.4% 'mentally unhealthy'. Being mentally healthy was associated with the highest functioning (greater social support and academic functioning) and being mentally unhealthy was associated with the worst. A one-unit increase (ranges=0-10) in peer support (OR 1.19; 95% CI 1.15 to 1.22), family support (OR 1.32; 95% CI 1.28 to 1.36), student support (OR 1.20; 95% CI 1.17 to 1.24) and average school marks (OR 1.18; 95% CI 1.10 to 1.27) increased the odds of being symptomatic yet content versus mentally unhealthy. Mentally healthy youth were the most likely to live with both parents (77% vs ≤65%) and report their family as well-off (62% vs ≤53%). CONCLUSIONS: We developed a novel, construct valid dual-factor measure of adolescent mental health. This potentially provides a nuanced and comprehensive approach to the assessment of adolescent mental health that is direly needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".