Global health competencies and approaches in medical education: a literature review
Bibliographic record
Abstract
BACKGROUND: Physicians today are increasingly faced with healthcare challenges that require an understanding of global health trends and practices, yet little is known about what constitutes appropriate global health training. METHODS: A literature review was undertaken to identify competencies and educational approaches for teaching global health in medical schools. RESULTS: Using a pre-defined search strategy, 32 articles were identified; 11 articles describing 15 global health competencies for undergraduate medical training were found. The most frequently mentioned competencies included an understanding of: the global burden of disease, travel medicine, healthcare disparities between countries, immigrant health, primary care within diverse cultural settings and skills to better interface with different populations, cultures and healthcare systems. However, no consensus on global health competencies for medical students was apparent. Didactics and experiential learning were the most common educational methods used, mentioned in 12 and 13 articles respectively. Of the 11 articles discussing competencies, 8 linked competencies directly to educational approaches. CONCLUSIONS: This review highlights the imperative to document global health educational competencies and approaches used in medical schools and the need to facilitate greater consensus amongst medical educators on appropriate global health training for future physicians.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".