Changing Rates of Self-Harm and Mental Disorders by Sex in Youths Presenting to Ontario Emergency Departments: Repeated Cross-Sectional Study
Bibliographic record
Abstract
Objective:To document the rates of intentional self-harm and mental disorders among youths aged 13 to 17 years visiting Ontario emergency departments (EDs) from 2003-2017.Methods:This was a repeated cross-sectional observational design. Outcomes were rates of adolescents with (1) at least 1 self-harm ED visit and (2) a visit with a mental disorder code.Results:Rates of youths with self-harm visits fell 32% from 2.6/1000 in 2003 to 1.8 in 2009 but rose 135% to 4.2 by 2017. The slope of the trend in self-harm visits changed from –0.18 youths/1000/year (confidence interval [CI], –0.24 to –0.13) during 2003 to 2009 to 0.31 youths/1000/year (CI, 0.27 to 0.35) during 2009 to 2017 (P < 0.001). Rates of youths with mental health visits rose from 11.7/1000 in 2003 to 13.5 in 2009 (15%) and to 24.1 (78%) by 2017. The slope of mental health visits changed from 0.22 youths/1000/year (CI, 0.02 to 0.42) during 2003 to 2009 to 1.84 youths/1000/year (CI, 1.38 to 2.30) in 2009 to 2017 (P < 0.001). Females were more likely to have self-harm (P < 0.001) and mental health visits (P < 0.001). Rates of increase after 2009 were greater for females for both self-harm (P < 0.001) and mental health (P < 0.001).Conclusions:Rates of adolescents with self-harm and mental health ED visits have increased since 2009, with greater increases among females. Research is required on the determinants of adolescents’ self-harm and mental health ED visits and how they can be addressed in that setting. Sufficient treatment resources must be supplied to address increased demands for services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".