The rationale and design of public involvement in health-funding decision making: focus groups with the Canadian public
Bibliographic record
Abstract
BACKGROUND: Worldwide, governments employ health technology assessment (HTA) in healthcare funding decision making. Requests to include public perspectives in this are increasing, with the idea being that the public can identify social values to guide policy development, increasing the transparency and accountability of government decision making. OBJECTIVE: To understand the perspectives of the Canadian public on the rationale and design of public involvement in HTA. DESIGN: A demographically representative sample of residents of a Canadian province was selected to take part in two sets of two focus groups (sixteen people for the first set and twenty for the second set). RESULTS: Participants were suspicious of the interests driving various stakeholders involved in HTA. They saw the public as uniquely impartial though also lacking knowledge about health technologies. Participants were also suspicious of personal biases and commended mechanisms to reduce their impact. Participants suggested various involvement methods, such as focus groups, citizens' juries and surveys, noting advantages and disadvantages belonging to each and commending a combination. DISCUSSION AND CONCLUSIONS: We identified a lack of public understanding of how decisions are made and distrust concerning whose interests and values are being considered. Public involvement was seen as a way of providing information to the public and ascertaining their views and values. Participants suggested that public involvement should employ a mixed-methods strategy to support informed debate and participation of a large number of people.
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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 | 0.215 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.036 | 0.022 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".