Health in All Policies Implementation at the Local Level: A Realist Explanatory Case Study
Bibliographic record
Abstract
Background: Health in All Policies (HiAP) implementation can occur at the national, regional, and local government levels; however, factors contributing or hindering HiAP implementation at the local level are largely unexplored. HiAP is an approach to public policy that considers health and health equity in the development, implementation, and evaluation of policies in various government sectors. By addressing health and health equity in all sectors such as the transportation, housing, education, and agriculture sectors, HiAP addresses the larger social determinants of health, ultimately improving population health and decreasing inequity. Implementation of HiAP is often idiosyncratic to specific settings. Therefore, when aiming to understand how HiAP is implemented locally, it is imperative to consider context. Methods: A literature review on HiAP implementation at the local level was initially conducted, resulting in seven themes significant to implementation. Three of those themes were subsequently tested in an explanatory case study using realist methods. Semi-structured interviews were conducted with ten government employees in the municipality of Kuopio, Finland. In addition to the key informant interviews, grey and peer-reviewed literature were also analyzed to understand how HiAP is implemented in Kuopio, Finland, and uncover the relevant facilitating and hindering factors. Results: Findings support the importance of three factors in successful HiAP implementation at the local level: having/creating a common goal, having dedicated staff and local leadership, and the use of Impact Assessments. Strong evidence was found for each hypothesis, including a description of the underlying mechanisms of how and why strategies for HiAP work. Numerous contextual factors relevant to implementation success in this setting were also uncovered, including a mature HiAP setting, a culture of intersectoral collaboration, and a small city milieu, among others. Conclusion: Results of this realist case study augment the limited evidence available on HiAP implementation locally. Findings contribute to the growing body of knowledge in this field by providing a focus on descriptions of underlying mechanisms for HiAP implementation strategies in a Finnish municipality. Evidence provided can also be used strategically by local policy and decision makers to improve HiAP implementation efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".