A glossary of terms for understanding political aspects in the implementation of Health in All Policies (HiAP)
Bibliographic record
Abstract
Health in All Policies (HiAP) is a strategy that seeks to integrate health considerations into the development, implementation and evaluation of policies across various non-health sectors of the government. Over the past 15 years, there has been an increase in the uptake of HiAP by local, regional and national governments. Despite the growing popularity of this approach, most existing literature on HiAP implementation remains descriptive rather than explanatory in its orientation. Moreover, prior research has focused on the more technical aspects of the implementation process. Thus, studies that aim to 'build capacity to promote, implement and evaluate HiAP' abound. Conversely, there is little emphasis on the political aspects of HiAP implementation. Neglecting the role of politics in shaping the use of HiAP is problematic, since health and the strategies by which it is promoted are partially political.This glossary addresses the politics gap in the existing literature by drawing on theoretical concepts from political, policy, and public health sciences to articulate a framework for studying how political mechanisms influence HiAP implementation. To this end, the glossary forms part of an on-going multiple explanatory case study of HiAP implementation, HARMONICS (HiAP Analysis using Realist Methods on International Case Studies, harmonics-hiap.ca), and is meant to expand on a previously published glossary addressing the topic of HiAP implementation more broadly. Collectively, these glossaries offer a conceptual toolkit for understanding how politics explains implementation outcomes of HiAP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.097 | 0.046 |
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".