Structured Global Health Programs in U.S. Medical Schools
Bibliographic record
Abstract
PURPOSE: To determine the prevalence and requirements of structured, longitudinal, nondegree global health (GH) programs (e.g., certificates, tracks, concentrations) in U.S. MD-granting medical schools. METHOD: In March 2011, two reviewers independently searched the Web sites of all 133 U.S. MD-granting medical schools and reviewed Google search results seeking evidence of, information about, and the requirements of structured GH programs. The authors excluded programs that were not open to medical students, granted a degree, and/or required medical students to extend training time. RESULTS: Of 133 institutions analyzed, 32 (24%) had evidence of a structured GH program. Of the 30 (94%) programs for which the authors could find further information online, 16/30 (53%) were administered by the medical school, whereas 13/30 (43%) were administered by a different entity within the university; 1/30 (3%) was jointly administered. All 30 of the programs required additional didactic course work. The median number of courses was 4 (range: 1-12). Of the 30 schools with GH programs, 22 (73%) required an international experiential component, but only 12/30 (40%) specifically required an international clinical experience. Only 1 school (3%) directly addressed language or cultural proficiency. CONCLUSIONS: Although structured GH programs were offered at one-quarter of U.S. medical schools, little standardization across programs existed in terms of requirements for didactic, clinical, scholarly, and cultural components. Online GH program information is not easily accessible, but it may be valuable in the development of new structured programs, the refinement of programs that already exist, and students' selection of medical schools.
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 | 0.022 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".