The Canadian Frailty Priority Setting Partnership: Research Priorities for Older Adults Living with Frailty
Bibliographic record
Abstract
BACKGROUND: Patient engagement in research priority-setting is intended to democratize research and increase impact. The objectives of the Canadian Frailty Priority Setting Partnership (PSP) were to engage people with lived or clinical experience of frailty, and produce a list of research priorities related to care, support, and treatment of older adults living with frailty. METHODS: The Canadian Frailty PSP was supported by the Canadian Frailty Network, coordinated by researchers in Toronto, Ontario and followed the methods of the James Lind Alliance, which included establishing a Steering Group, inviting partner organizations, gathering questions related to care, support and treatment of older adults living with frailty, processing the data and prioritizing the questions. RESULTS: In the initial survey, 799 submissions were provided by 389 individuals and groups. The 647 questions that were within scope were categorized, merged, and summarized, then checked against research evidence, creating a list of 41 unanswered questions. Prioritization took place in two stages: first, 146 individuals and groups participated in survey and their responses short-listed 22 questions; and second, an in-person workshop was held on September 26, 2017 in Toronto, Ontario where these 22 questions were discussed and ranked. CONCLUSION: Researchers and research funders can use these results to inform their agendas for research on frailty. Strategies are needed for involving those with lived experience of frailty in research.
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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 | 0.110 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".