Covid-19 and Priorities for Research in Aging
Bibliographic record
Abstract
This article describes priority areas for research on the impact of the Covid-19 pandemic on older adults that have been identified by the CIHR Institute of Aging (CIHR-IA). The process used by CIHR-IA consists of several iterative phases and thus far has resulted in identification of three key areas for Covid-19 research needs and four cross-cutting thematic areas. The key research priority areas are as follows: response of older adults to disease, vaccination, and therapeutics; mental health and isolation; and supportive care environments. The four cross-cutting themes are equity, diversity, and inclusion (EDI); ethical/moral considerations; evidence-informed practices; and digital health technologies. The priorities outlined in this article will inform CIHR-IA's responses to Covid-19 research needs.
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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 | 0.382 | 0.290 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.025 | 0.040 |
| Scholarly communication | 0.040 | 0.037 |
| Open science | 0.007 | 0.048 |
| Research integrity | 0.036 | 0.036 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".