Participation in health care priority-setting through the eyes of the participants
Bibliographic record
Abstract
OBJECTIVES: The literature on participation in priority-setting has three key gaps: it focuses on techniques for obtaining public input into priority-setting that are consultative mechanisms and do not involve the public directly in decision-making; it focuses primarily on the public's role in priority-setting, not on all potential participants; and the range of roles that various participants play in a group making priority decisions has not been described. To begin addressing these gaps, we interviewed individuals who participated on two priority-setting committees to identify key insights from participants about participation. METHODS: A qualitative study consisting of interviews with decision-makers, including patients and members of the public. RESULTS: Members of the public can contribute directly to important aspects of priority-setting. The participants described six specific priority-setting roles: committee chair, administrator, medical specialist, medical generalist, public representative and patient representative. They also described the contributions of each role to priority-setting. CONCLUSIONS: Using the insights from decision-makers, we have described lessons related to direct involvement of members of the public and patients in priority-setting, and have identified six roles and the contributions of each role.
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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 | 0.045 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".