Growing academic global surgery: opportunities for Canadian trainees
Bibliographic record
Abstract
Global surgery has seen exponential growth over the past few years, and Canadian trainees' interest in the field has followed. Global surgery is defined by a commitment to health equity and community partnership. Engagement with its core principles is relevant for all Canadian surgical trainees and offers a perspective into inequities in surgical access and outcomes for patients and communities, both locally and globally. Several opportunities in academic global surgery for trainees have emerged in Canada, but appear to be underutilized. This article highlights existing Canadian global surgery initiatives, including formal postgraduate curricula, research and policy collaborations, trainee networks, advocacy projects, dedicated fellowships, and conferences. We identify areas in which institutions and departments of surgery can better support trainees in exploring each of these categories during training. Canadian trainees' exposure to global surgery can nurture their roles as future health advocates, communicators, and leaders locally and beyond.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.002 |
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".