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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".