Trends in self-harm visits to the emergency department among children and youth before and during the COVID-19 pandemic in Alberta: An interrupted time series analysis
Bibliographic record
Abstract
Abstract Objectives This study aims to describe and compare the rate of self-harm visits to the emergency department (ED) among children and youth in Alberta, before and after the COVID-19 pandemic. Methods Data from 2010 to 2022 were obtained from administrative databases held at Alberta Health Services, which capture all ED visits across Alberta. An interrupted time series was implemented using autoregressive moving average models. Results Findings indicate a step decrease in self-harm visits at pandemic onset (−2.94 patients per 10,000; P = 0.008). However, the rate of increase in self-harm visits remained consistent with the prepandemic rate of visits (slope change: 0.047 per 10,000, P = 0.24). There were also significant step changes found among males and females, several age subsets and among rural and urban subsets, but there were no significant slope changes among any subgroups. Conclusion Overall, findings indicate that the rate of self-harm visits following the onset of the pandemic did not differ from that which would be expected based on the upward trend in the 10 years before the pandemic, and this finding was consistent when stratifying by sex, age, and rurality. There was a significant reduction in self-harm visits at the onset of the COVID-19 pandemic.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".