Using CanMEDS to guide international health electives: an enriching experience in Uganda defined for a Canadian surgery resident.
Bibliographic record
Abstract
BACKGROUND: Surgery residents who wish to travel during their residency will often seek an elective experience in a low-or middle-income country. Objectives for international health electives (IHEs) are often vague and poorly defined. Further, feedback to, and evaluation of, the resident after the IHE are often not specific because international preceptors are not familiar with the desired educational outcomes of Canadian residency programs. Residents who choose an elective in a low-income country usually anticipate that they will contribute some medical service to an existing impoverished health care system, and in this setting, they hope to gain exposure to a high operative volume with potentially fewer institutional and administrative obstacles. METHODS: In this paper, we describe one resident's elective experience in Mbarara, Uganda. In addition to her clinical experience, the resident performed a retrospective audit of surgical admissions. After her elective, we asked the resident to reflect on her experience and to use the Canadian Medical Education Directives for Specialists (CanMEDS) framework to describe the challenges she encountered and to define the learning outcomes gained with respect to each CanMEDS role. RESULTS: We discovered that the resident had a rich and insightful educational experience when discussed in this context. As a result, we have created a guide for structuring postgraduate IHEs around the CanMEDS roles, using them to ask pre-and postelective questions to develop relevant and practical IHE objectives. CONCLUSION: We propose that this guide has the potential to improve both resident preparation before international experience and also subsequent evaluation of resident performance in this ill-defined area. More important, we found that IHEs are a useful vehicle to evaluate resident achievement of the CanMEDS competencies in a way that is reflective, realistic and representative of the multiple challenges involved when working in international health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".