Status of global health fellowship training in the United States and Canada
Bibliographic record
Abstract
BACKGROUND: Increasing numbers of residency graduates desire global health (GH) fellowship training. However, the full extent of training options is not clear. OBJECTIVE: To identify clinical GH fellowships in all specialties in the U.S. and Canada and to describe their demographics, innovative features, and challenges. METHODS: The authors surveyed program directors or designees from GH fellowships with a web-based tool in 2017. RESULTS: The authors identified 85 programs. Fifty-four programs (63.5%) responded confirming 50 fellowships. One- third of fellowships accepted graduates from more than one specialty, and the most common single-specialty programs were Emergency Medicine and Family Medicine. Fellowships most commonly were 24 months in duration with a median size of one fellow per year. Funding and lack of qualified applicants were significant challenges. Most programs were funded through fellow billing for patient care or other self-support. CONCLUSION: The number of U.S. and Canadian GH fellowship programs has nearly doubled since 2010. Challenges include lack of funding and qualified applicants. Further work is needed to understand how best to identify and disseminate fellowship best practices to meet the diverse needs of international partners, fellows, and the patients they serve and to determine if consensus regarding training requirements would be beneficial.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".