Advancing Open‐Access Education for the Surgical Team Worldwide: The Development and Rollout of the United Nations Global Surgery Learning Hub (SURGhub)
Bibliographic record
Abstract
PROBLEM: Training and professional development programs for surgeons, anesthetists, obstetricians, and perioperative nurses in low-resource settings are often constrained by lack of access to appropriate training materials. Potential learners looking to access online content are faced with internet connectivity issues, difficulties in finding and accessing resources, resources that are inappropriate for their context and training, and opaque content quality control processes. APPROACH: The United Nations Global Surgery Learning Hub (SURGhub) was launched on June 28, 2023 to address this need. SURGhub curates high-quality surgical, anesthetic, obstetric, and perioperative nursing e-learning courses and makes them freely available on one integrated online platform, optimized for low-bandwidth settings. It is a product of the global surgery community, powered by over 200 volunteers and anchored in the United Nations. OUTCOMES: In little over 18 months since its launch, SURGhub has enrolled 11,451 registered learners from 190 countries. Fifty-five percent of users are based in low- or lower-middle income countries. Learners can access 76 interactive e-learning courses, provided by 20 different institutions. Median course user rating is 4.6/5. DISCUSSION: SURGhub is addressing the needs of underserved surgical learners through innovative, participatory technological solutions. To address the unmet need, SURGhub must expand its educational offering, including through the addition of new content types, personalized learning, and increased provision of content in languages other than English. The translation of SURGhub educational content into improvements in clinical practice and patient outcomes must be measured.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".