Rethinking the politics and implementation of health in all policies
Bibliographic record
Abstract
In Europe, successful health policies have contributed to a continued decline in mortality. However, not all parts of Europe have benefited equally and the sustainability of achievements cannot be taken for granted since health policies vary widely even among neighbouring countries. Furthermore, there are a number of remaining public health challenges such as food and alcohol polices. We argue that if we are to make further progress we need to rethink the politics and implementation of Health in All Policies. Commenting on an article analyzing the roll out and early implementation of Israel's National Programme to Promote Active, Healthy Lifestyles provides an opportunity to thrash out four issues. First, intersectoral structures are key transmission belts for Health in All Policies between ministries and sectors and we need to exploit their specific uses and understand their limitations. Second, our analytical perspective should focus on what it takes to introduce policy change instead of assuming an idealized policy cycle. This includes a reconsideration of interventions which may not be very effective but help to raise the standing of health on the political agenda, thus providing a stronger basis for policy change. Third, we need to better understand variations in context between and within countries, e.g. why do some countries adopt Health in All policies but others don't, and why is it that in the same country compliance with some health policies is better than with others. Finally, we will need to better understand how a diverse set of actors from other sectors can internalize health as an intrinsic value.
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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 | 0.058 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.058 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".