The rules of the game: Healthcare systems and cross-national attitudes about healthcare provision
Bibliographic record
Abstract
In recent years, healthcare typologies are increasingly scrutinized. Prevailing healthcare system categorizations draw on comparative–institutional welfare state arrangements that constitute the “rules of the game” for healthcare provision. Challenging these perspectives, health policy perspectives suggest that ongoing policy changes shifted the “rules of the game” in ways that are not adequately captured by traditional comparative–institutional typologies. As a result, new questions arise about which categorization is most salient for understanding public attitudes about healthcare. We adjudicate between these two perspectives by examining the association between healthcare system typology and two different and important types of attitudes about healthcare provision: government responsibility and spending. Using hierarchical linear models, we find that traditional welfare state conceptions of healthcare systems are more closely associated with public opinions about healthcare provision. In general, respondents in countries with healthcare systems that have greater state involvement and rely more on public financing, which are traditional, institutional–comparative factors, report greater support for government responsibility in and spending on healthcare. We highlight how rallying broad public support for changes to healthcare systems in the wake of the COVID-19 pandemic will require that researchers and policy makers understand what the public has come to expect about healthcare, as well as the institutional arrangements around healthcare that set the “rules of the game.”
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".