Meeting CanMEDS objective through global health education at MUN
Bibliographic record
Abstract
Background The academic and practical benefit of global health education have been recognized and endorsed by the Association of Faculties of Medicine in Canada (AFMC) and articulated by the AFMC Resource Group (2011). Clinical skill acquired in low resource contexts are augmented by a broader understanding of both the social determinants of health and the importance of health advocacy, one of the CanMEDS key roles for physicians. New accreditation standards set out by the LCME and endorsed by IFMS, include pre-departure training for international electives, debriefing post elective and emphasize patient and trainee safety.i Together, these developments provided the impetus for a Global Health Office at MUN. Objectives We demonstrate the value of a Global Health curriculum in Undergraduate Medical Education through programs based on Can MEDS core competencies and key physician roles. These include medical expert, communicator, collaborator, manager, health advocate, scholar, and professional. We hope to demonstrate that through integrating global health education trainees will identify barriers to health equity; identify social, political and economic determinants of health; and develop skills for health and human rights advocacy. These objectives also promote social accountability in medical education as part of MCCQE preparation. Methods For the past two years one of us (SM) has participated in the AFMC-GHRG crafting core competencies for international electives. This group meets regularly and has conducted a survey of all universities that offer global health electives for medical students and residents. Building on this work we are establishing best practices and core competencies in global health programming a t MUN and strengthening alignments with existing academic programs in the Faculty of Medicine as well as the Aboriginal Health Initiative and Gateway Project. Conclusions: The commitment to global health programs is an increasingly globalized world with diverse populations, and to meet the challenges of working in under-resourced health care settings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.247 | 0.048 |
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".