Quantifying the Impact of COVID-19 School Closures on Mental Health of School-Aged Children, Adolescent and Young Adults in Ontario: A Bayesian Time-Series and Hierarchical Analysis
Bibliographic record
Abstract
The COVID-19 pandemic led to prolonged school closures in Ontario, which disrupted daily routines, peer relationships, and access to school-based supports for children, adolescents, and young adults. We assessed the association between cumulative pandemic school closure and mental health-related hospital, and emergency room visits among individuals aged 5-24 years in Ontario. Linked administrative health data included 330,645 inpatient and emergency or ambulatory visits from March 2020 to February 2024. Bayesian hierarchical negative binomial regression models estimated associations, adjusted for trends, seasonality, socioeconomic indicators, and population size, with stratification by age, sex, care settings, pandemic phases, and public health unit (PHU) characteristics. During COVID-19, greater cumulative pandemic school closure (cPSC) was linked to clear age- and sex-specific differences in mental health service use. Early- late mid-adolescent females showed the largest increases with longer closures, while young adult males declined. These trends were consistent across care settings. In the post-vaccine period (March 2021-February 2022), overall rates fell, but adolescent females remained the highest users. In the two-year recovery phase (post Covid), the influence of cPSC lessened, yet use remained concentrated among adolescent females, young adult males stayed lower.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".