Self-harm among youth during the first 28 months of the COVID-19 pandemic in Ontario, Canada: a population-based study
Bibliographic record
Abstract
BACKGROUND: Youth have reported worsening mental health during the COVID-19 pandemic. We sought to evaluate rates of pediatric acute care visits for self-harm during the pandemic according to age, sex and mental health service use. METHODS: We conducted a population-based, repeated cross-sectional study using linked health administrative data sets to measure monthly rates of emergency department visits and hospital admissions for self-harm among youth aged 10-17 years between Jan. 1, 2017, and June 30, 2022, in Ontario, Canada. We modelled expected rates of acute care visits for self-harm after the pandemic onset based on prepandemic rates. We reported relative differences between observed and expected monthly rates overall and by age group (10-13 yr and 14-17 yr), sex and mental health service use (new and continuing). RESULTS: In this population of about 1.3 million children and adolescents, rates of acute care visits for self-harm during the pandemic were higher than expected for emergency department visits (0.27/1000 population v. 0.21/1000 population; adjusted rate ratio [RR] 1.29, 95% confidence interval [CI] 1.19-1.39) and hospital admissions (0.74/10 000 population v. 0.43/10 000 population, adjusted RR 1.72, 95% CI 1.46-2.03). This increase was primarily observed among females. Rates of emergency department visits and hospital admissions for self-harm were higher than expected for both those aged 10-13 years and those aged 14-17 years, as well as for both those new to the mental health system and those already engaged in care. INTERPRETATION: Rates of acute care visits for self-harm among children and adolescents were higher than expected during the first 2 and a half years of the COVID-19 pandemic, particularly among females. These findings support the need for accessible and intensive prevention efforts and mental health supports in this population.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".