Engaging the public in priority‐setting for health technology assessment: findings from a citizens’ jury
Bibliographic record
Abstract
OBJECTIVES: To assess the feasibility of using a citizens' jury to elicit public values on health technologies and to develop criteria for setting priorities for health technology assessment (HTA). METHODS: Sixteen individuals were selected from 1600 randomly sampled residents of the Capital Health Region in Alberta, Canada. They participated in a 2 (1/2) day jury which comprised presentations by 'expert witnesses', who represented innovators, patients, health-care policy-makers and clinicians, as well as a series of small and large group priority-setting exercises based on actual examples of technologies that had recently been considered for assessment by local and national HTA bodies. The session was audio-taped, and transcripts were independently reviewed by two researchers using content analytical techniques in order to ensure that no important concepts expressed by individual jurors were missed during group development of the final list of priority-setting criteria. Jurors evaluated the process by completing self-administered, semi-structured questionnaires at the end of the session. Responses were analysed using qualitative methods. RESULTS: The jury identified 13 criteria, which they subsequently ranked in order of importance. The top two criteria included 'potential to benefit a number of people' and 'extends life with quality'. Based on feedback from questionnaires, jurors valued the opportunity to become engaged in such a process, and expressed interest in participating in future juries. CONCLUSIONS: Citizens' juries offer a feasible approach to involving the public in priority-setting for HTA. Furthermore, technologies that may benefit a number of people and improve quality of life appear to be of greatest importance to the public.
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 | 0.045 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".