Réaction rapide face à la COVID-19 : relever les défis et améliorer la préparation mentale du personnel de sécurité publique
Bibliographic record
Abstract
Points saillants• La pandémie de COVID-19 a mis en évidence le rôle essentiel du personnel de sécurité publique dans le service et la protection de l'en semble des Canadiens.• Le personnel de sécurité publique signalant déjà des problèmes de santé mentale et de bienêtre avant même la pandémie de COVID-19, les nouveaux facteurs de stress pourraient augmenter les besoins en ressources supplémentaires chez ce personnel afin qu'il soit capable de nous aider à long terme.• Cet article propose divers éléments de soutien pouvant constituer des pistes pertinentes en vue de favo riser le bienêtre du personnel de sécurité publique pendant la pé riode de stress prolongé provoqué par la pandémie de COVID-19.• Les autosoins peuvent s'avérer essentiels au maintien de la santé mentale et du bienêtre du person nel de sécurité publique pendant la pandémie de COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".