Inside the black box of comparative national healthcare performance in 35 OECD countries: Issues of culture, systems performance and sustainability
Bibliographic record
Abstract
BACKGROUND: Is national healthcare performance associated with country-level characteristics, and if so what are the implications for international health policy? METHODS AND FINDINGS: We compared Hofstede's six cultural dimensions against relative health systems performance of 35 countries. Hierarchical cluster analysis identified best-matched groupings of countries. Performance was measured by the Organisation for Economic Co-operation and Development's (OECD's) Health at a Glance indicators data framework (five dimensions with 57 indicators) and the United Nations' (UNs') Sustainability Development Goals (SDG) data set (15 indicators). Three country clusters emerged: Collective-Pyramidal (n = 9: comprising Slovak Republic, Mexico, Poland, Greece, Spain, Turkey, Portugal, Chile, and Slovenia); Collaborative-Networked (n = 12: UK, Canada, Australia, USA, Ireland, New Zealand, Netherlands, Finland, Iceland, Norway, Denmark, and Sweden); and Orderly-Future Orientated (n = 14: Korea, Estonia, Latvia, Austria, Israel, Japan, Czech Republic, Hungary, Italy, Belgium, France, Germany, Luxembourg and Switzerland). The Collaborative-Networked cluster had significantly better performing health systems measured by both the Health at a Glance and SDG performance data, followed by the Orderly-Future Orientated cluster, followed by the Collective-Pyramidal cluster. The Collaborative-Networked Cluster was characterized by low power distance (e.g., greater levels of equity), low uncertainty avoidance (e.g., toleration of others' opinions), individualism (e.g., self-reliance) and indulgence (e.g., drives and norms to enjoy life and have fun). CONCLUSIONS: National cultures are associated with healthcare performance on two key international measures. In national and international efforts to improve health system performance, cultural characteristics play an important role. This information may be of value to regulators, policymakers, researchers and clinicians examining the practical impact of culture on healthcare performance.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".