Comparative study of health systems of liberal, social, and mixed countries based on CLA framework
Bibliographic record
Abstract
Background and Objectives: Different governance approaches have various definitions and systems about health. The purpose of this study was to compare the appropriateness of the health system performance with the ideology of the selected countries. Methods: In this comparative study, liberal countries (America, Canada, France), social countries (Russia, China, Cuba) and mixed countries (Sweden, Norway, England) were selected purposefully. Data were obtained from World Bank and WHO’s published documents and discourse literature studies. Causal layered analysis framework was used for data analysis. Results: Comparison of health indicators showed that mixed countries were in a better position than the other two groups. The health system’s stewardship of the liberal, mixed, and social countries were decentralized, semi-centralized, and centralized, respectively. Discourses of the liberal states were based on the capitalist economy, with lack of reliance on natural resources. Socialist countries, a socialist economy system emphasizes the use of natural resources. In these countries governmental involvement is maximum. Mixed countries have a constitutional monarchy government and benefit from both of these approaches to create welfare based on the ideology of liberalism and the welfare state approach. Conclusion: Mixed countries with appropriate economic- social conditions, semi-centralized structure of service delivery, suitable financing system, and regional and local management of services (highlighting the role of municipalities), have better health status than other countries. The ideology of the countries forms the social, economic, and political structures as well health. Iran should consider various layers of metaphor, discourse, casual structures, and litany for redesigning the health system.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".