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 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 | 0.097 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".