Intersectoriality in Danish municipalities: corrupting the social determinants of health?
Bibliographic record
Abstract
Action on the social determinants of health (SDH) through intersectoral policymaking is often suggested to promote health and health equity. This paper argues that the process of intersectoral policymaking influences how the SDH are construed and acted upon in municipal policymaking. We discuss how the intersectoral policy process legitimates certain practices in the setting of Danish municipal health promotion and the potential impact this can have for long-term, sustainable healthy public policy. Based on ethnographic fieldwork, we show how the intention of intersectoriality produces a strong concern for integrating health into non-health sectors to ensure productive collaboration. To encourage this integration, health is often framed as a means to achieve the objectives of non-health sectors. In doing so, the intersectoral policy process tends to favor smaller-scale interventions that aim to introduce healthier practices into various settings, e.g. creating healthy school environments for increased physical activity and healthy eating. While other more overarching interventions on the health impacts of broader welfare policies (e.g. education policy) tend to be neglected. The interventions hereby neglect to address more fundamental SDH. Based on these findings, we argue that intersectoral policymaking to address the SDH may translate into a limited approach to action on so-called 'intermediary determinants' of health, and as such may end up corrupting the broader SDH. Further, we discuss how this corruption affects the intended role of non-health sectors in tackling the SDH, as it may impede the overall success and long-term sustainability of intersectoral efforts.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".