Using Win-Win Strategies to Implement Health in All Policies: A Cross-Case Analysis
Bibliographic record
Abstract
BACKGROUND: In spite of increasing research into intersections of public policy and health, little evidence shows how policy processes impact the implementation of Health in All Policies (HiAP) initiatives. Our research sought to understand how and why strategies for engaging partners from diverse policy sectors in the implementation of HiAP succeed or fail in order to uncover the underlying social mechanisms contributing to sustainable implementation of HiAP. METHODS: In this explanatory multiple case study, we analyzed grey and peer-review literature and key informant interviews to identify mechanisms leading to implementation successes and failures in relation to different strategies for engagement across three case studies (Sweden, Quebec and South Australia), after accounting for the role of different contextual conditions. FINDINGS: Our results yielded no support for the use of awareness-raising or directive strategies as standalone approaches for engaging partners to implement HiAP. However, we found strong evidence that mechanisms related to "win-win" strategies facilitated implementation by increasing perceived acceptability (or buy-in) and feasibility of HiAP implementation across sectors. Win-win strategies were facilitated by mechanisms related to several activities, including: the development of a shared language to facilitate communication between actors from different sectors; integrating health into other policy agendas (eg., sustainability) and use of dual outcomes to appeal to the interests of diverse policy sectors; use of scientific evidence to demonstrate the effectiveness of HiAP; and using health impact assessment to make policy coordination for public health outcomes more feasible and to give credibility to policies being developed by diverse policy sectors. CONCLUSION: Our findings enrich theoretical understanding in an under-unexplored area of intersectoral action. They also provide policy makers with examples of HiAP across wealthy welfare regimes, and improve understanding of successful HiAP implementation practices, including the win-win approach.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".