Integrating public input into healthcare priority-setting decisions
Bibliographic record
Abstract
Decision makers are pressed to involve the public in priority setting. However, public input is only one form of evidence. So, how can information from the public be combined with other knowledge? The authors qualitatively analysed articles that explicitly address this question. We identified the other forms of information that tend to be used in conjunction with public input, the degree to which members of the public are asked to be the integrators of data, and techniques that recur in several settings. Three factors must be balanced when integrating public opinion into priority setting: first, balancing problem-solving and sense-making objectives; second, choosing between consensus-building and structured-conflict approaches; third, addressing many broad factors or a smaller set of focused alternatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.349 | 0.515 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.029 | 0.025 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".