Assessing the impact of the COVID-19 pandemic on the mental health–related hospitalization rate of youth in Canada: an interrupted time series analysis
Bibliographic record
Abstract
Introduction This study evaluated the effect of the COVID-19 pandemic on temporal trends in mental health and addiction–related inpatient hospitalization rates among youth (aged 10–17 years) in Canadian provinces and territories (excluding Quebec) from 1 April 2018 to 5 March 2022. Methods We conducted an interrupted time series analysis across three periods: T0 (pre-pandemic: 1 April 2018 to 15 March 2020); T1 (early pandemic: 15 March 2020 to 5 July 2020); and T2 (later pandemic: 6 July 2020 to 5 March 2022). Results Pre-pandemic mental health and addiction–related hospitalization rates had significant regional variability, with weekly rates from 6.27 to 85.59 events per 100 000 persons in Manitoba and the territories combined, respectively. During T1, the national (excluding Quebec) weekly hospitalization rate decreased from a pre-pandemic level of 12.82 (95% CI: 12.14 to 13.50) to 5.11 (95% CI: 3.80 to 6.41) events per 100 000 persons. There was no statistically significant change in the mental health and addiction– related hospitalization rate across provinces and territories in T2 compared to T0. However, there was a significant increase in the rate of self-harm–related hospitalizations among females Canada-wide and in most provinces during this period. Conclusion Although several Canadian studies have reported increases in mental health and addiction–related outpatient and emergency department visits among youth during the COVID-19 pandemic, this did not correspond to an increase in the inpatient hospital burden, with the notable exception of self-harm among young females.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.002 | 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".