COVID-19 au Canada : impact et conséquences sur la santé mentale et les soins
Bibliographic record
Abstract
L’humanité a toujours connu des crises sanitaires plus ou moins marquantes dans l’histoire. La pandémie du COVID-19 a rapidement submergé les systèmes de soins de santé de plusieurs pays. Les réponses des gouvernements au COVID-19 ont varié considérablement. Malgré les mesures adoptées, des effets néfastes sur la santé mentale sont inévitables, en conséquence des mesures sanitaires limitant les contacts et les interactions sociales et à la récession économique. La pandémie a accéléré l’implémentation de la téléconsultation/télémédecine et a forcé les gouvernements à repenser des stratégies alternatives pour rendre des services de santé mentale. Cet article examine l'impact potentiel de la pandémie du COVID-19 sur la santé mentale, en particulier au Canada, et les enjeux associés à la téléconsultation pendant la pandémie qui est devenue une condition préalable à la prestation continue de soins de santé mentale dans de nombreux contextes.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".