Training global surgery advocates: Strengthening the global surgery voice
Bibliographic record
Abstract
Objective: To strengthen medical trainees around the world on global surgery and advocacy and help develop future generations of global surgeons, anaesthesiologists, and obstetricians.Design: Training Global Surgery Advocates (TGSA), a standardized three-day advocacy workshop developed by the International Student Surgical Network (InciSioN), was built on traditional didactic lectures, role-play exercises, small working group activities, and advocacy and diplomacy training. Assessment was done using a 5-point Likert scale for 18 components regarding the perceived familiarity, knowledge, and motivation for global surgery.Setting: The training was given in the context of the pre-general assembly of the International Federation of Medical Students Associations (IFMSA) at Université Laval, in Quebec City, Canada.Participants: Twenty-five participants were selected to attend the workshop from a pool of 52 applicants, of which 14 medical students from 7 high-income countries and 7 low- and middle-income countries.Results: An average increase of 1.73 points across all 18 workshop components was observed among participants. After the workshop, all participants agreed or strongly agreed (4.64 average) on their motivation to train other medical students in their respective countries to become global surgery advocates.Conclusion: TGSA significantly improved participants’ knowledge and advocacy skills underlying global surgery. A mixed didactic and hands-on workshop appears to be feasible, enjoyable for participants, and effective in improving medical students involvement in the emerging field of global surgery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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, 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".