Public deliberation for health system decision-making: An evaluative case study of the McMaster Health Forum’s citizen panels
Bibliographic record
Abstract
Background: Public deliberation can be used in a number of decision-making processes to make the health system more responsive to public values, and to help inform and refine health system policy decisions. This study evaluates how the McMaster Health Forum implements the key elements of public deliberation and identifies both areas of strength and potential areas for improvement. Methods: An evaluative case study approach was used. Data were collected from three sources: quantitative and open-ended responses to questionnaires from 19 panels (200 respondents); panel summaries from 13 panels; and transcripts of 2 panel deliberations. Thematic analysis was used to assess four key elements of deliberation: the representativeness of participants, the information supports provided to them, the procedural criteria used, and the focus on explicit reasoning in coming to conclusions. Results: Participants felt that the McMaster Health Forum recruited a representative sample of participants based on gender and diversity of opinion. However, participants noted that the panels could be improved by striving for more age and ethnocultural diversity while also including health professionals or policymakers. Participants mostly occupied the role of a ‘consumer’ of health services. They viewed the information presented in citizen briefs as credible but had questions about the brief-development process. Procedurally, the panels fostered openness without impeding consensus and facilitators fostered mutual respect among participants. Finally, the groups incorporated values, showed an ability to come to a deeper understanding of policy options and harnessed the diverse experiences of their fellow participants as they reasoned. Discussion: This case study is part of a larger evaluation process that assesses all of the McMaster Health Forum citizen panels which aim to elicit citizens’ values and preferences about health system issues in Canada. The framework used to assess the public deliberation process can be used to evaluate other processes in the future.
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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 | 0.088 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.020 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".