International Consensus on Global Surgery Learning Objectives and Competencies
Bibliographic record
Abstract
OBJECTIVE: This project aimed to achieve international consensus on core learning objectives for global surgery education. BACKGROUND: As global surgery emerges as an academic field, there is a growing need for consensus-driven learning objectives to guide education and training. Existing curricula vary widely and lack multidisciplinary input. METHODS: A modified Delphi consensus was conducted with an international panel of global surgery experts. A scoping review informed an initial list of learning objectives, categorized into 14 domains based on the Consortium of Universities for Global Health framework. Panelists rated objectives over three iterative survey rounds, with consensus defined as ≥80% agreement within ±1 Likert point of the median. RESULTS: Sixty-one experts from 26 countries across all World Health Organization (WHO) regions participated, representing surgery (40.1%), anesthesia (14.8%), obstetrics and gynecology (14.8%), general practitioners with and without enhanced surgical skills (16.4%), and allied health fields (6.6%). The majority (57.4%) had over 10 years of experience in global surgery. Across three Delphi rounds, 120 learning objectives reached consensus, covering key domains such as the global burden of surgical disease, surgical system strengthening, ethics and equity, health policy, and sustainable development. A total of 25 (20.8%) objectives were designated for introductory learners, 55 (45.8%) for advanced learners, and 40 (33.3%) for both levels. CONCLUSION: This Delphi consensus provides a structured, globally relevant framework for global surgery education, supporting curriculum development and competency-based training. These findings underscore the importance of aligning global surgery education with evolving healthcare priorities while ensuring adaptability across diverse surgical contexts.
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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 | 0.205 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".