Are We There Yet? Preparing Canadian Medical Students for Global Health Electives
Bibliographic record
Abstract
PURPOSE: To understand the current landscape and the evolution of predeparture training (PDT) in Canadian medical education. METHOD: The authors surveyed one faculty and one student global health leader at each of Canada's 17 medical schools in February 2008 and May 2010 to assess the delivery of and requirements for PDT at each institution. The authors then used descriptive statistics to compare responses across schools and years. RESULTS: In 2008, one faculty and one student representative from each of the 17 Canadian medical schools completed the survey; in 2010, 17 faculty and 16 student representatives responded. The number of medical schools offering PDT grew substantially from 2008 to 2010 (11/17 [65%] versus 16/17 [94%]). Three of the five new programs in 2010 were student run. The number of schools with mandatory PDT nearly doubled (6/17 [35%] versus 11/17 [65%]). However, institutional funding remained scarce, as 10 of 16 programs had budgets of less than $500 in 2010. PDT content, frequency, and format varied from school to school. CONCLUSIONS: Medical students have been responsible for organizing the majority of new PDT. To ensure quality and sustainability, however, faculty must play a more central role in the planning and implementation of such training programs. Medical schools must continue to reevaluate how best to maximize global health electives for trainees and the communities in which they study. PDT offers one avenue for schools to ensure that students are safe and socially accountable during their time abroad.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".