Mental well-being trends and school-based protective factors among adolescents in British Columbia (2015–2022): A population-based study
Bibliographic record
Abstract
BACKGROUND: Adolescent mental well-being has declined in the past decade. Much research relies on administrative data and population-based research incorporating youth voices and exploring protective factors for mental well-being is scarce. This study examined trends in adolescent mental well-being from 2015 to 2022 in British Columbia (BC), Canada. We examined sex differences in the trends, the role of protective factors in school, and the relative importance of protective factors for mental well-being. METHODS: We drew from self-report data from eight years of implementation (2015-2022) of the Middle Years Development Instrument (MDI) with grade 7 students (N = 69,391; 49 % girls) in schools. Positive (satisfaction with life; SWL) and negative (depressive symptoms) mental well-being indicators were examined over time using a repeated cross-sectional design. Analyses were stratified by sex. The presence of 0-3 protective factors (adult support at school, peer belonging, school connectedness) and SES were covariates. RESULTS: SWL significantly declined and depressive symptoms significantly increased across the study period and for most adjacent study years. Girls had significantly lower well-being and a steeper decline than boys. For the subset of students who scored high on all protective factors, the decline in well-being was attenuated but not eliminated and the sex gap was reduced. CONCLUSIONS: The decline in well-being and the protective nature of modifiable protective factors identified in our study highlight the need for population-level mental health strategies that can be implemented in partnership with school districts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".