Engaging patients in health research: identifying research priorities through community town halls
Bibliographic record
Abstract
BACKGROUND: The vision of Canada's Strategy for Patient-Oriented Research is that patients be actively engaged as partners in health research. Support units have been created across Canada to build capacity in patient-oriented research and facilitate its conduct. This study aimed to explore patients' health research priorities in the province of Newfoundland and Labrador (NL). METHODS: Eight town halls were held with members of the general public in rural and urban settings across the province. Sessions were a hybrid information-consultation event, with key questions about health research priorities and outcomes guiding the discussion. RESULTS: Sixty eight members of the public attended town hall sessions. A broad range of health experiences in the healthcare system were recounted. Key priorities for the public included access and availability of providers and services, disease prevention and health promotion, and follow-up support and community care. In discussing their health research priorities, participants spontaneously raised a broad range of suggestions for improving the healthcare system in our jurisdiction. CONCLUSIONS: Public research priorities and suggestions for improving the provision of healthcare provide valuable information to guide Support Units' planning and priority-setting processes. A range of research areas were raised as priorities for patients that are likely comparable to other healthcare systems. These create a number of health research questions that would be in line with public priorities. Findings also provide lessons learned for others and add to the evidence base on patient engagement methods.
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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 | 0.120 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.031 | 0.017 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.006 | 0.044 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".