Public Engagement through the Toronto Health Policy Citizens Council: What do Citizens Value in Health Care?
Bibliographic record
Abstract
Health policy making is fraught with difficult decisions that result from conflicts between people’s values. Citizens are important stakeholders in this process, and it is through methods of public engagement that they can be involved in developing health policy. Deliberative forms, in particular, have the ability to improve decision quality and promote greater acceptance of decisions. This study used the Toronto Health Policy Citizens Council to examine citizens’ values on 7 specific health policy questions asked over a two-year period. A thematic analysis was performed on the transcript content derived from the audiotaped deliberations from Council meetings. Nineteen values were identified. The results suggest that it may be a combination of factors of the health policy topic discussed that shapes the values elicitation seen in this kind of public engagement. In conclusion, citizens councils appear effective at eliciting citizens’ values, and are a good way to actively educate participants about health care.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".