Strengthening the implementation of Health in All Policies: a methodology for realist explanatory case studies
Bibliographic record
Abstract
To address macro-social and economic determinants of health and equity, there has been growing use of intersectoral action by governments around the world. Health in All Policies (HiAP) initiatives are a special case where governments use cross-sectoral structures and relationships to systematically address health in policymaking by targeting broad health determinants rather than health services alone. Although many examples of HiAP have emerged in recent decades, the reasons for their successful implementation--and for implementation failures--have not been systematically studied. Consequently, rigorous evidence based on systematic research of the social mechanisms that have regularly enabled or hindered implementation in different jurisdictions is sparse. We describe a novel methodology for explanatory case studies that use a scientific realist perspective to study the implementation of HiAP. Our methodology begins with the formulation of a conceptual framework to describe contexts, social mechanisms and outcomes of relevance to the sustainable implementation of HiAP. We then describe the process of systematically explaining phenomena of interest using evidence from literature and key informant interviews, and looking for patterns and themes. Finally, we present a comparative example of how Health Impact Assessment tools have been utilized in Sweden and Quebec to illustrate how this methodology uses evidence to first describe successful practices for implementation of HiAP and then refine the initial framework. The methodology that we describe helps researchers to identify and triangulate rich evidence describing social mechanisms and salient contextual factors that characterize successful practices in implementing HiAP in specific jurisdictions. This methodology can be applied to study the implementation of HiAP and other forms of intersectoral action to reduce health inequities involving multiple geographic levels of government in diverse settings.
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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 | 0.106 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".