Health care system performance of 27 OECD countries
Bibliographic record
Abstract
The article is based on a multidimensional conception of healthcare system performance. Our objectives are to assess the performance of the healthcare systems of 27 Organisation for Economic Co-operation and Development (OECD) countries and to discern the countries' profiles according to the homogeneity of their healthcare systems' levels of performance. The analyses were carried out on data collected from the 27 high-income OECD countries, primarily using the OECD Health Data 2007 database, the World Health Organization 2008 statistics, OECD Health at a Glance and OECD Social Indicators. Each healthcare system's performance was assessed on the basis of the volume of available resources, services produced and health outcomes achieved and efficiency, effectiveness and productivity, thus characterizing the investments made in proportion to the available resources and services produced. Overall performance profiles were constructed taking into account simultaneously the level of all these components. Using multiple clusters analysis, we were able to group the countries into four profiles (satisfactory, promising, weak-polarized and limited) according to the homogeneity of their performance levels. This article offers a broad overview of the performance of these healthcare systems. The results will enable decision-makers to know the strengths and weaknesses of their own health care system and also to compare it with those of other countries.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".