International Plastic Surgery Missions
Bibliographic record
Abstract
Residency education has shifted over the past decade in an attempt to graduate well-rounded physicians. There is a recognition that a physician's abilities must extend beyond medical knowledge. The Royal College of Physicians and Surgeons of Canada introduced the CanMEDS physician competency framework in 2005. The framework provides 7 areas of competencies that are aimed at providing improved patient care. These competencies are medical expert, communicator, collaborator, manager, health advocate, scholar, and professional. Teaching and evaluating many of these competencies is often challenging for residency training programs. We believe that international surgical missions provide a prime opportunity to teach and evaluate all CanMEDS' roles.Plastic surgery is a field with many different organizations involved in international surgery. Many plastic surgery training programs offer opportunities for residents to become involved in these international surgical missions. Through these trips, residents gain surgical experience, see a variety and volume of clinical cases, and have the opportunity to travel to a foreign country and experience different cultures. We believe that international plastic surgery surgical missions also provide an exceptional micro environment for the teaching of CanMEDS roles. Using examples from residents' personal experiences on international plastic surgery missions to China, Mali, and Cambodia, we describe the benefits of these missions in transferring the CanMEDS competencies to resident training.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.062 | 0.007 |
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".