Politics, welfare regimes, and population health: controversies and evidence
Bibliographic record
Abstract
In recent years, a research area has emerged within social determinants of health that examines the role of politics, expressed as political traditions/parties and welfare state characteristics, on population health. To better understand and synthesise this growing body of evidence, the present literature review, informed by a political economy of health and welfare regimes framework, located 73 empirical and comparative studies on politics and health, meeting our inclusion criteria in three databases: PubMed (1948-), Sociological Abstracts (1953-), and ISI Web of Science (1900-). We identified two major research programmes, welfare regimes and democracy, and two emerging programmes, political tradition and globalisation. Primary findings include: (1) left and egalitarian political traditions on population health are the most salutary, consistent, and substantial; (2) the health impacts of advanced and liberal democracies are also positive and large; (3) welfare regime studies, primarily conducted among wealthy countries, find that social democratic regimes tend to fare best with absolute health outcomes yet consistently in terms of relative health inequalities; and (4) globalisation defined as dependency indicators such as trade, foreign investment, and national debt is negatively associated with population health. We end by discussing epistemological, theoretical, and methodological issues for consideration for future research.
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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 | 0.038 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".