Building a Framework for Global Health Learning
Bibliographic record
Abstract
PURPOSE: This study set out to explore the current state of global health concentrations in Canadian medical schools and to solicit feedback on the barriers and challenges to implementing rigorous global health concentration programs. METHOD: A set of consensus guidelines for global health concentrations was drafted through consultation with student and faculty leaders across Canada between May 2011 and May 2012. Drawing on these guidelines, a formal survey was sent to prominent faculty at each of the 14 English-speaking Canadian medical schools. A thematic analysis of the results was then conducted. RESULTS: Overall, the guidelines were strongly endorsed. A majority of Canadian medical schools have programs in place to offer global health course work, extracurricular learning opportunities, local community service-learning, low-resource-setting clinical electives, predeparture training, and postreturn debriefing. Although student evaluation, global health mentorship, and knowledge translation projects were endorsed as important components, few schools had been successful in implementing them. Language training for global health remains contested. Other common critiques included a lack of time and resources, and difficulties in setting standards for student evaluation. CONCLUSIONS: The results suggest that these guidelines are appropriate and, at least for the major criteria, achievable. Although many Canadian schools offer individual components, the majority of schools have yet to develop formally structured concentration programs. By better articulating guidelines, a standardized framework can aid in the establishment and refinement of future programs.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.055 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.017 | 0.115 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".