Stumbling from One Disaster to Another: \nThe COVID-19 Pandemic and Mental Health \nCalls for Police Service across Canada
Bibliographic record
Abstract
The COVID-19 pandemic had a significant impact on crime in Canada and internationally. However, less is known about the impact of the pandemic on police-reported mental-health-related incidents. We explore three types of mental-health-related incidents (suicide and suicide attempts, Mental Health Act apprehensions, and mental health [other]) against property and violent crimes, across 13 police jurisdictions in Canada. Despite an international decline in most crime types during COVID-19, we find general stability across police-reported mental-health-related incidents. These findings suggest that the change in social behaviour that reduced opportunities for crime did not have a similar effect on mental-health-related incidents. It also suggests that calls for increased police budgets to respond to expected increases in mental-health-related incidents may be unjustified. Résumé: La pandémie de COVID-19 a eu un impact considérable sur la criminalité au Canada et à l'international. On ne sait pas grand-chose cependant de l'impact de la pandémie sur les incidents liés à la santé mentale rapportés par la police. Nous explorons ici trois types d'incidents liés à la santé mentale (suicides et tentatives de suicide, arrestations dans le cadre de la Loi sur la santé mentale, santé mentale [autre]) dans le contexte de crimes contre les biens et de crimes violents, dans les territoires desservis par 13 corps policiers au Canada. Malgré une diminution à l'international de la plupart des types de crimes pendant la pandémie, nos résultats montrent une stabilité générale des incidents liés à la santé mentale rapportés par la police. Cela suggère que les changements de comportement social qui ont fait diminuer les occasions de commettre des infractions n'ont pas eu le même effet sur les incidents liés à la santé mentale. Cela suggère également que les appels à augmenter les budgets de la police afin de répondre aux hausses attendues des incidents liés à la santé mentale ne sont peut-être pas fondés.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".