A Realist Explanatory Case Study Investigating How Common Goals, Leadership, and Committed Staff Facilitate Health in All Policies Implementation in the Municipality of Kuopio, Finland
Bibliographic record
Abstract
BACKGROUND: Health in All Policies (HiAP) encompasses collaboration across government and the consideration of health in various governmental sector's policies and decisions. Despite increasing advocacy, interest, and uptake in HiAP globally, empirical and evaluative studies are underrepresented in this growing literature, particularly literature on HiAP implementation at the local level. Finland has been a pioneer in and champion for HiAP. METHODS: A realist explanatory case study design was used to test hypotheses about how HiAP is implemented in Kuopio, Finland. Semi-structured interviews with ten government employees from various sectors were conducted. Data from interviews and literature were analyzed with the aims of uncovering explanatory mechanisms in the form of context-strategy-mechanism-outcome (CSMO) configurations related to implementation strategies. Evidence was evaluated for quality based on triangulation of sources and strength of evidence. We hypothesized that having or creating a common goal between sectors and having committed staff and local leadership would facilitate implementation. RESULTS: Strong evidence supports our hypothesis that having or creating a common goal can aid in positive implementation outcomes at the local level. Common goals can be created by the strategies of having a city mandate, engaging in cross-sectoral discussions, and/or by working together. Policy and political elite leadership led to HiAP implementation success because leaders supported HiAP work, thus providing justification for using time to work intersectorally. How and why the wellbeing committee facilitated implementation included by providing opportunities for discussion and learning, which led to understanding of how non-health decisions impact community wellbeing, and by acting as a conduit for the communication of wellbeing goals to government employees. CONCLUSION: At the municipal level, having or creating a common goal, leadership from policy and political elites, and the presence of committed staff can facilitate HiAP implementation. Inclusion of not only strategies for HiAP, but also the explanatory mechanisms, aids in elucidating how and why HiAP is successfully implemented in a local setting.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".