Ambulance services attendance for mental health and overdose before and during COVID-19: an interrupted time series comparison of regions in Canada and the United Kingdom (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED Background: The COVID-19 pandemic impacted mental health and healthcare systems globally. This study examined its impact on ambulance attendances for mental health and overdose, comparing similar regions in the United Kingdom (UK) and Canada that implemented different public health measures. Methods: An interrupted time series study using 182,497 ambulance attendance records for mental health and overdose in the UK (East Midlands region) and Canada (Hamilton and Niagara regions) from Jan 1, 2019 to July 31, 2020. Negative binomial regressions modelled the count of attendances per week per 100 000 population prior to the pandemic, at lockdown, and in the weeks following lockdown. Stratified analyses were conducted by sex and age. Results: Ambulance attendances for mental health and overdose had very small week-over-week increases prior to lockdown (e.g., IRR=1.002, 95%CI 1.002-1.003 for mental health [UK]). However, substantial changes were observed at the time of lockdown; while the rate of overdose attendances significantly dropped in the study regions of both countries (IRR=0.573, 95%CI 0.518-0.635 [UK]; IRR=0.743, 95%CI 0.602-0.917 [Canada]), the rate of mental health attendances increased in the UK region only (IRR=1.125, 95%CI 1.031-1.227 [UK]; IRR=0.922, 95%CI 0.794-1.071 [Canada]). Different trends were observed based on sex and age category within and between study regions. Conclusions: The observed changes in ambulance attendances for mental health and overdose at the time of lockdown differed between the UK and Canada study regions. These results may inform future pandemic planning and further research on the public health measures that may explain observed regional differences.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".