La COVID-19 et les priorités de recherche sur le vieillissement
Bibliographic record
Abstract
Cet article présente les domaines prioritaires de recherche sur les impacts de la pandémie de COVID-19 chez les personnes âgées telles qu'ils ont été identifiés par l'Institut du vieillissement des IRSC (IV-IRSC). Le processus utilisé par l'IV-IRSC a comporté plusieurs phases itératives qui ont permis d'identifier trois secteurs prioritaires parmi les besoins de la recherche relative à la COVID-19, et quatre axes thématiques transversaux. Les secteurs de recherche prioritaires sont : 1) la réponse des personnes âgées à la maladie, à la vaccination et aux traitements, 2) la santé mentale et l'isolement, et 3) les milieux de soins soutenants. Les quatre thèmes transversaux sont : a) l'Équité, la diversité et l'inclusion (EDI), b) les considérations éthiques et morales, c) les pratiques fondées sur les données probantes, et d) les technologies numériques de la santé. Les priorités décrites dans cet article guideront les réponses de l'IV-IRSC aux besoins de la recherche sur la COVID-19.
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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 | 0.146 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".