Health in All Policies-The Finnish Initiative: Background, Principles, and Current Issues
Bibliographic record
Abstract
Many historical developments, such as the Alma Ata Declaration or the Ottawa Charter, have drawn attention to the need for intersectoral work and for considering the health aspects of different policy proposals. In the 1970s, Finland started broad actions to change national diets to reduce the high mortality associated with cardiovascular diseases (CVDs). This and other work in Finland have involved many sectors and policies, resulted in significant public health improvements, and paved the way for the Health in All Policies (HiAP) initiative started during the Finnish European Union (EU) presidency in 2006. The initiative and the principles have encouraged further developments in Finland and have been linked with related developments within the EU and the World Health Organization (WHO).
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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 | 0.034 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.027 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".