Comparing Iran's Healthcare System Efficiency with OECD Countries Using Data Envelopment Analysis
Bibliographic record
Abstract
Background: The health sector is one of the most important service sectors and one of the indicators of development and social welfare. The aim of this study is to evaluate the efficiency of Iranchr('39')s health system compared to developed countries.Methods: This study used data envelopment analysis to evaluate efficiency. All members of the Organization for Economic Co-operation and Development (OECD), along with Iran, were considered as the decision units in the analysis. Outputs are life expectancy at birth and infant mortality rate; and inputs are health expenditure, number of physicians, and number of hospital beds. DEA Solver software was used for the analysis.Results: The most efficient countries in terms of the health system are Canada, Chile, Estonia, Iceland, Ireland, Israel, Japan, South Korea, Latvia, Luxembourg, Mexico, Slovenia, Spain, Switzerland, Turkey, and Iran. Their inefficiency was calculated using the axial output model. The most inefficient countries were Portugal, Germany, the United States, Poland, the Czech Republic, Slovakia and Hungary.Conclusion: Iranchr('39')s health system was found efficient, which showed that in terms of life expectancy and infant mortality rate (2 important markers of the health system), Iran performed efficiently comparing to its inputs, health expenditures, physicians, and the hospital bed. However, the Iranian health system was more efficient in this method due to the fewer inputs (physician and hospital bed) with similar outputs to other countries (life expectancy and infant mortality rate). On the other hand, the outbreak of the coronavirus showed that the health systems of the countries should be prepared for such pandemics and be able to increase the number of hospital beds, physicians, and other health system inputs.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".