Social inequalities in youth mental health in Canada, 2007–2022: a population-based repeated cross-sectional study
Bibliographic record
Abstract
PURPOSE: Rising concern surrounds youth mental health in Canada, with growing disparities between females and males. However, less is known about recent trends by other sociodemographic factors, including sexual orientation, ethnocultural background, and socioeconomic status. METHODS: This study analyzed data from 96 683 youths aged 15-24 who participated in the nationally representative Canadian Community Health Survey (CCHS) between 2007 and 2022. Trends in absolute and relative inequalities in poor/fair self-rated mental health (SRMH) by sex, sexual orientation, racialized and Indigenous identity, and socioeconomic conditions were assessed. RESULTS: The percent of youths reporting poor/fair SRMH quadrupled from 4.3% in 2007-08 to 20.1% in 2021-22. During the same period, absolute inequalities in SRMH increased by 9.9% points (95% CI: 6.6, 12.9) for females compared to males, 11.4% points (95% CI: 4.6, 18.2) for Indigenous versus non-racialized youth, and 15.4% points (95% CI: 5.7, 25.1) for youth (aged 18-24) identifying as lesbian, gay, or bisexual (LGB) compared to heterosexual. CONCLUSION: The sustained deterioration in youth SRMH over the past decade and a half has been accompanied by widening inequalities across several dimensions important for health equity in Canada. Action is needed to identify and implement effective programs and policies to support youth mental health and address disparities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".