Advancing Strategies for Agenda Setting by Health Policy Coalitions: A Network Analysis of the Canadian Chronic Disease Prevention Survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Health in all policies can address chronic disease morbidity and mortality by increasing population-level physical activity and healthy eating, and reducing tobacco and alcohol use. Both governmental and nongovernmental policy influencers are instrumental for health policy that modifies political, economic, and social environments. Policy influencers are informed and persuaded by coalitions that support or oppose changing the status quo. Empirical research examining policy influencers' contact with coalitions, as a social psychological exposure with health policy outcomes, can benefit from application of health communication theories. Accordingly, we analyzed responses to the 2014 Chronic Disease Prevention Survey for 184 Canadian policy influencers employed in provincial governments, municipalities, large workplaces, school boards, and the media. In addition to contact levels with coalitions, respondents' jurisdiction, organization, and ideology were analyzed as potential moderators. Calculating authority score centrality using network analysis, we determined health policy supporters to be more central in policy influencer networks, and theorized their potential to impact health policy public agenda setting via priming and framing processes. We discuss the implications of our results as presenting opportunities to more effectively promote health policy through priming and framing by coordinating coalitions across risk behaviors to advance a societal imperative for chronic disease prevention.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it