Determining Research Priorities Through Partnership with Patients: An Overview
Bibliographic record
Abstract
There is an increasing level of emphasis being placed on health care providers and funders to incorporate patient-centered care into research. Involving patients and caregivers in establishing research priorities ensures the relevance of the research produced. Priority setting is a process that can be used to produce a robust set of research questions that researchers can address over the coming years. One of the methods for determining research priorities that involves patients, caregivers and clinicians is the James Lind Alliance priority setting partnership model. This method is focused on being exclusive, transparent, and evidence-based. Using a recent example of patients on or nearing dialysis, we highlight the key steps to assess research priorities in patients, caregivers and clinicians: (i) formation of a steering committee to guide the overall process; (ii) form priority setting partnerships; (iii) identify and gather research uncertainties; (iv) process and collate submitted research uncertainties; and (v) final priority setting workshop to determine the top 10 research priorities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".