Public health policy research: making the case for a political science approach
Bibliographic record
Abstract
The past few years have seen the emergence of claims that the political determinants of health do not get due consideration and a growing demand for better insights into public policy analysis in the health research field. Several public health and health promotion researchers are calling for better training and a stronger research culture in health policy. The development of these studies tends to be more advanced in health promotion than in other areas of public health research, but researchers are still commonly caught in a naïve, idealistic and narrow view of public policy. This article argues that the political science discipline has developed a specific approach to public policy analysis that can help to open up unexplored levers of influence for public health research and practice and that can contribute to a better understanding of public policy as a determinant of health. It describes and critiques the public health model of policy analysis, analyzes political science's specific approach to public policy analysis, and discusses how the politics of research provides opportunities and barriers to the integration of political science's distinctive contributions to policy analysis in health promotion.
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 | 0.242 | 0.209 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.017 | 0.148 |
| Scholarly communication | 0.045 | 0.042 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.035 | 0.039 |
| Insufficient payload (model declined to judge) | 0.005 | 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".