Global Health Values of a Multidirectional Near Peer Training Program in Surgery, Pathology, Anatomy, Research Methodology, and Medical Education for Haitian, Rwandan, and Canadian Medical Students
Bibliographic record
Abstract
BACKGROUND: As health care delivery increasingly requires providers to cross international borders, medical students at McGill University, Canada, developed a multidirectional exchange program with Haiti and Rwanda. The program integrates surgery, pathology, anatomy, research methodology, and medical education. OBJECTIVE: The aim of the present study was to explore the global health value of this international training program to improve medical education within the environment of developing countries, such as Haiti and Rwanda, while improving sociocultural learning of Canadian students. METHODS: Students from the University of Kigali, Rwanda and Université Quisqueya, Haiti, participated in a 3-week program at McGill University. The students spanned from the first to sixth year of their respective medical training. The program consisted of anatomy dissections, surgical simulations, clinical pathology shadowing, and interactive sessions in research methodology and medical education. To evaluate the program, a survey was administered to students using a mixed methodology approach. FINDINGS: Common benefits pointed out by the participants included personal and professional growth. The exchange improved career development, sense of responsibility toward one's own community, teaching skills, and sociocultural awareness. The participants all agreed that the anatomy dissections improved their knowledge of anatomy and would make them more comfortable teaching the material when the returned to their university. The clinical simulation activities and shadowing experiences allowed them to integrate the different disciplines. However, the students all felt the research component had too little time devoted to it and that the knowledge presented was beyond their educational level. CONCLUSION: The development of an integrated international program in surgery, pathology, anatomy, research methodology, and medical education provided medical students with an opportunity to learn about differences in health care and medical education between the 3 countries. This exchange demonstrated that a crosscultural near-peer teaching environment can be an effective and sustainable method of medical student-centered development in global health.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".