Advancing Strategies for Agenda Setting by Health Policy Coalitions: A Network Analysis of the Canadian Chronic Disease Prevention Survey
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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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".