A scoping and web-based review of current practices and lessons learnt in development and sustainability of global health emergency medicine fellowships
Bibliographic record
Abstract
Background: Despite a high perceived interest in advanced global health training among Canadian emergency medicine trainees, only one global health emergency medicine (GHEM) fellowship existed in Canada at the time of this review. We conducted a scoping and web-based review to summarize the components of, and lessons learnt through development and implementation of global health emergency medicine fellowship programs to date, to inform program development. Methods: We conducted a scoping and web-based review by systematically searching electronic databases from inception to 2021 for articles and websites (2022) describing global health emergency medicine training programs based in high income countries. Results: From 2957 articles and 62 websites identified, eight articles and 43 websites were included in the review. Fellowships are generally structured as follows: 1–2 years duration curriculum including clinical skills, and course and field work focused on education, research or administration, funded by fellows’ clinical hours. Details on trainees’ experiences, international work, and program outcomes were lacking. Conclusions: This review highlights the need for information on lessons learnt through development and implementation of GHEM fellowship programs, and experiences and outcomes of trainees to date, to inform program improvements to optimize the benefits of GHEM fellowship training. Registration: Open science framework; https://doi.org/10.17605/OSF.IO/UAH35 February 19 th , 2018.
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 | 0.044 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.032 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".