Bibliographic record
Abstract
BACKGROUND: Citizen deliberation is a prominent theme in health policy literature. It is believed that citizens who deliberate may influence the setting of public health-care priorities. Currently, in some jurisdictions, citizens are members of community health boards, and thus have a forum to articulate and share values that could affect the reduction of health inequalities within their communities. However, there is little conceptual clarity on the character of citizen deliberation, or, more specifically, how citizens may articulate and share values. OBJECTIVES: This paper reviews the literature on citizen deliberation in setting health-care priorities; discusses potential challenges for citizens in setting health-care priorities; outlines a developing theory of citizen deliberation; describes how citizens may articulate and share values that ground their health-care priorities and outlines implications of a developing theory of citizen deliberation, its relevance to UK study findings, and to community health boards in setting health-care priorities. CONCLUSIONS: As members of community health boards, citizens can evaluate their subjective experiences. In reasoning about embedded values, citizens may gain insight into the kind of community they aspire to be, and, in that process, examine their intentions, including whether to serve self or other(s). Citizens who articulate and share values such as respect, generosity or equity may justify health-care priorities that create opportunities for all community members to gain mastery over their lives.
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 | 0.025 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".