Hospitalizations and emergency department visits for self-harm in Canada during the first two years of the COVID-19 pandemic: A time series analysis
Bibliographic record
Abstract
BACKGROUND: Rates of hospitalizations and emergency department (ED) visits due to self-harm are important indicators for understanding the impact of the COVID-19 pandemic on mental health. The objective of this study was to assess changes in self-harm hospitalizations and ED visits in Canada during the first two years of the pandemic. METHODS: Rates of self-harm hospitalizations and ED visits during the pandemic were predicted based on regression analyses that modeled trends over a 5-year pre-pandemic period from fiscal year 2015 to 2019. The ratios of observed and model predicted (expected) rates in 2020 and 2021 were estimated separately to assess changes during the pandemic. RESULTS: Overall, rates of self-harm hospitalizations and ED visits were lower than expected during the pandemic, especially in 2020. In 2021, rates for females returned to near-expected levels; but they remained lower than expected for males. Females aged 10-14 years had higher than expected rates. The rate ratio of observed rate over expected rate was 1.2 in 2020 but further increased to 1.8 in 2021 for both hospitalizations and ED visits. Higher than expected rates were also observed among females aged 15-19 years in 2021 only. LIMITATIONS: Suicide attempts and non-suicidal self-harm cases could not be distinguished. CONCLUSIONS: We observed lower than or close to expected rates of self-harm hospitalizations and ED visits during the pandemic for most population groups. The increased rates for young females highlights the importance of continued surveillance post-pandemic and targeted mental health services and suicide prevention programs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".