Health in All Policies: From rhetoric to implementation and evaluation - the Finnish experience
Bibliographic record
Abstract
The principles of the Health in All Policies (HiAP) approach are not new. Their international roots can be traced back to 1978 and the Alma-Ata Declaration and the 1986 Ottawa Charter. In Finland, the roots of HiAP go back to 1972 when the Economic Council of Finland, chaired by the Prime Minister, launched the 'Report of the working group exploring the goals of health'. The paper discusses the history, rationale, and implementation of the principles underlying the umbrella concept of HiAP. A rationale for implementing a new concept - HiAP in 2006 during the Finnish European Union presidency - is given. The focus here will be on implementation of HiAP. International material supporting the implementation is introduced and practical examples from Finland presented. The Benchmarking System for Health Promotion Capacity Building is introduced, since it has been used as a primary source of information for monitoring and evaluating HiAP in Finland at the local level. The experience from Finland clearly indicates that HiAP as an approach and as a way of working requires long-term commitment and vision. For working across sectors it is crucial to have data on health and health determinants and analyses of the links between health outcomes, health determinants, and policies across sectors and levels of governance. Intersectoral structures, processes, and tools for the identification of problems and solutions, decisions, and implementation across sectors are prerequisites of HiAP. Legislative backing has proven to be useful, especially in providing continuation and sustainability.
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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 | 0.086 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.016 | 0.035 |
| Scholarly communication | 0.030 | 0.016 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".