Welfare regimes, population health and health inequalities: a research synthesis
Bibliographic record
Abstract
BACKGROUND: Research on the social determinants of health is increasingly using welfare regime theory. Although a key argument is that population health will be better and health inequalities lower in social democratic regimes than in others, this research has not been subjected to a systematic review. This paper identifies and assesses empirical studies that explicitly use a welfare regime typology in comparative health research. METHODS: 15 electronic databases and relevant bibliographies were searched to identify empirical studies published in English-language journals from January 1970 to February 2011. Thirty-three studies appearing in 14 peer-reviewed journals between 1994 and 2011 met the inclusion criteria. RESULTS: Three welfare regime typologies and their variants dominated existing work, which consisted of two broad study types: One compared population health and health inequalities across welfare regimes; the other considered relationships between health and the political determinants and policies of welfare regimes. Studies were further distinguished by the presence or absence of statistical significance testing of relationships of interest. Just under one half of studies comparing outcomes by regime found at least some evidence that health inequalities were lowest or population health was the best in social democratic countries. Studies analysing the relationship between health (mortality) and the political determinants or policies of welfare states were more likely to report results consistent with welfare regime theory. CONCLUSIONS: Health differences by regime were not always consistent with welfare regime theory. Measurement of policy instruments or outcomes of welfare regimes may be more promising for public health research than the use of typologies alone.
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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 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.024 | 0.032 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".