Implementing Health in All Policies – Time and Ideas Matter Too!; Comment on 'Understanding the Role of Public Administration in Implementing Action on the Social Determinants of Health and Health Inequities'
Bibliographic record
Abstract
Carey and Friel suggest that we turn to knowledge developed in the field of public administration, especially new public governance, to better understand the process of implementing health in all policies (HiAP). In this commentary, I claim that theories from the policy studies bring a broader view of the policy process, complementary to that of new public governance. Drawing on the policy studies, I argue that time and ideas matter to HiAP implementation, alongside with interests and institutions. Implementing HiAP is a complex process considering that it requires the involvement and coordination of several policy sectors, each with their own interests, institutions and ideas about the policy. Understanding who are the actors involved from the various policy sectors concerned, what context they evolve in, but also how they own and frame the policy problem (ideas), and how this has changed over time, is crucial for those involved in HiAP implementation so that they can relate to and work together with actors from other policy sectors.
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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 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.095 | 0.083 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".