How and why do win–win strategies work in engaging policy-makers to implement Health in All Policies? A multiple-case study of six state- and national-level governments
Bibliographic record
Abstract
BACKGROUND: Much of the research about Health in All Policies (HiAP) implementation is descriptive, and there have been calls for more evaluative evidence to explain how and why successes and failures have occurred. In this cross-case study of six state- and national-level governments (California, Ecuador, Finland, Norway, Scotland and Thailand), we tested hypotheses about win-win strategies for engaging policy-makers in HiAP implementation drawing on components identified in our previous systems framework. METHODS: We used two sources of data - key informant interviews and peer-reviewed and grey literature. Using a protocol, we created context-mechanism-outcome pattern configurations to articulate mechanisms that explain how win-win strategies work and fail in different contexts. We then applied our evidence for all cases to the systems framework. We assessed the quality of evidence within and across cases in terms of triangulation of sources and strength of evidence. We also strengthened hypothesis testing using replication logic. RESULTS: We found robust evidence for two mechanisms about how and why win-win strategies build partnerships for HiAP implementation - the use of shared language and the value of multiple outcomes. Within our cases, the triangulation was strong, both hypotheses were supported by literal and contrast replications, and there was no support against them. For the third mechanism studied, using the public-health arguments win-win strategy, we only found evidence from Finland. Based on our systems framework, we expected that the most important system components to using win-win strategies are sectoral objectives, and we found empirical support for this prediction. CONCLUSIONS: We conclude that two mechanisms about how and why win-win strategies build partnerships for HiAP implementation - the use of shared language and the value of multiple outcomes - were found as relevant to the six settings. Both of these mechanisms trigger a process of developing synergies and releasing potentialities among different government sectors and these interactions between sectors often work through sectoral objectives. These mechanisms should be considered when designing future HiAP initiatives and their implementation to enhance the emergence of non-health sector policy-makers' engagement.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".