Medical Schools in Fragile States: Implications for Delivery of Care
Bibliographic record
Abstract
OBJECTIVE: To report on medical schools in fragile states, countries with severe development challenges, and the impact on the workforce for health care delivery. DATA SOURCES: 2007 and 2012 World Bank Harmonized List of Fragile Situations; 1998-2012 WHO Global Health Observatory; 2014 World Directory of Medical Schools. DATA EXTRACTION: Fragile classification established from 2007 and 2012 World Bank status. Population, gross national income, health expenditure, and life expectancy were 2007 figures. Physician density was most recently available from WHO Global Health Observatory (1998-2012), with number of medical schools from 2014 World Directory of Medical Schools. STUDY DESIGN: Regression analyses assessed impact of fragile state status in 2012 on the number of medical schools in 2014. PRINCIPAL FINDINGS: Fragile states were 1.76 (95 percent CI 1.07-2.45) to 2.37 (95 percent CI 1.44-3.30) times more likely to have fewer than two medical schools than nonfragile states. CONCLUSIONS: Fragile states lack the infrastructure to train sufficient numbers of medical professionals to meet their population health needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".