Do health policy advisors know what the public wants? An empirical comparison of how health policy advisors assess public preferences regarding smoke-free air, and what the public actually prefers
Bibliographic record
Abstract
BACKGROUND: Health policy-making, a complex, multi-factorial process, requires balancing conflicting values. A salient issue is public support for policies; however, one reason for limited impact of public opinion may be misperceptions of policy makers regarding public opinion. For example, empirical research is scarce on perceptions of policy makers regarding public opinion on smoke-free public spaces. METHODS: Public desire for smoke-free air was compared with health policy advisor (HPA) perception of these desires. Two representative studies were conducted: one with the public (N = 505), and the other with a representative sample of members of Israel's health-targeting initiative, Healthy Israel 2020 (N = 34), in December 2010. Corresponding questions regarding desire for smoke-free areas were asked. Possible smoke-free areas included: 100% smoke-free bars and pubs; entrances to health facilities; railway platforms; cars with children; college campuses; outdoor areas (e.g., pools and beaches); and common areas of multi-dweller apartment buildings. A 1-7 Likert scale was used for each measure, and responses were averaged into a single primary outcome, DESIRE. Our primary endpoint was the comparison between public preferences and HPA assessment of those preferences. In a secondary analysis, we compared personal preferences of the public with personal preferences of the HPAs for smoke-free air. RESULTS: HPAs underestimated public desire for smoke-free air (Public: Mean: 5.06, 95% CI:[4.94, 5.17]; HPA: Mean: 4.06, 95% CI:[3.61, 4.52]: p < .0001). Differences at the p = .05 level were found between HPA assessment and public preference for the following areas: 100% smoke-free bars and pubs; entrances to healthcare facilities; train platforms; cars carrying children; and common areas of multi-dweller apartment buildings. In our secondary comparison, HPAs more strongly preferred smoke-free areas than did the public (p < .0001). CONCLUSIONS: Health policy advisors underestimate public desire for smoke-free air. Better grasp of public opinion by policy makers may lead to stronger legislation. Monitoring policy-maker assessment of public opinion may shed light on incongruities between policy making and public opinion. Further, awareness of policy-maker misperceptions may encourage policy-makers to demand more accurate information before making policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".