A SHARP Response: Developing COVID-19 Research Aims in Partnership with the Seniors Helping as Research Partners (SHARP) Group
Bibliographic record
Abstract
COVID-19 has disproportionally impacted older adults, and has highlighted many issues, including extreme deficiencies in Canadian long-term care homes and gaps in home and community care services for older adults. In recent years, there has been a push towards better patient and family engagement in health system research, and with the onset of the pandemic, engaging older adults in research and policy planning is more important than ever. In this article, we describe the Seniors Helping as Research Partners (SHARP) approach to engagement with older adults as an example of how partnerships that engage older adults in the development of research aims and processes can help to ensure that future research meets the needs of older adults. SHARP members highlighted a number of areas for future COVID-19 research such as improvements to long-term care, enhancing access to home and community care, and a focus on aging and social isolation.
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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 | 0.166 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.010 | 0.032 |
| Research integrity | 0.060 | 0.080 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".