Economic considerations and health in all policies initiatives: evidence from interviews with key informants in Sweden, Quebec and South Australia
Bibliographic record
Abstract
BACKGROUND: Health in All Policies (HiAP) is a form of intersectoral action that aims to include the promotion of health in government initiatives across sectors. To date, there has been little study of economic considerations within the implementation of HiAP. METHODS: As part of an ongoing program of research on the implementation of HiAP around the world, we examined how economic considerations influence the implementation of HiAP. By economic considerations we mean the cost and financial gain (or loss) of implementing a HiAP process or structure within government, or the cost and financial gain (or loss) of the policies that emerge from such a HiAP process or structure. We examined three jurisdictions: Sweden, Quebec and South Australia. Semi-structured telephone interviews were conducted with 12 to 14 key informants in each jurisdiction. Two investigators separately coded transcripts to identify relevant statements. RESULTS: Initial readings of transcripts led to the development of a coding framework for statements related to economic considerations. First, economic evaluations of HiAP are viewed as important for prompting HiAP and many forms of economic evaluation were considered. However, economic evaluations were often absent, informal, or incomplete. Second, funding for HiAP initiatives is important, but is less important than a high-level commitment to intersectoral collaboration. Furthermore, having multiple sources of funding of HiAP can be beneficial, if it increases participation across government, but can also be disadvantageous, if it exposes underlying tensions. Third, HiAP can also highlight the challenge of achieving both economic and social objectives. CONCLUSIONS: Our results are useful for elaborating propositions for use in realist multiple explanatory case studies. First, we propose that economic considerations are currently used primarily as a method by health sectors to promote and legitimize HiAP to non-health sectors with the goal of securing resources for HiAP. Second, allocating resources and making funding decisions regarding HiAP are inherently political acts that reflect tensions within government sectors. This study contributes important insights into how intersectoral action works, how economic evaluations of HiAP might be structured, and how economic considerations can be used to both promote HiAP and to present barriers to implementation.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".