VitalSurg: Outcomes From a Surgical Task-Shifting Training Program in a Humanitarian Context
Bibliographic record
Abstract
OBJECTIVE: The Vital Surgery Training Program (VitalSurg) is a task-shifting initiative designed to build local surgical capacity in surgical deserts by training generalist doctors to perform essential procedures. Implemented in partnership with Médecins Sans Frontières (MSF), the program has undergone 2 pilot iterations in South Sudan. This study evaluates the second iteration, which reflects a refined curriculum and integrated assessment strategy informed by lessons learned from the first iteration. We sought to examine the feasibility, effectiveness, and adaptability of competency-based surgical training embedded within humanitarian clinical care. DESIGN: We conducted a mixed-methods summative evaluation of the training program. Quantitative trainee performance data-including pre- and postmodule quizzes, oral and written exams, case logs, and Entrustable Professional Activities (EPAs)-were analyzed alongside qualitative data from Key Informant Interviews (KIIs) with trainers, trainees, and MSF stakeholders. SETTING: This study was conducted at Aweil State Hospital, an MSF-supported district hospital in Northern Bahr El Ghazal, South Sudan, which provides maternity and pediatric surgical care in a resource-limited setting. PARTICIPANTS: Two local medical doctors were enrolled in the second VitalSurg cohort. RESULTS: Over the 18-month training program, trainees performed an average of 1305 procedures and improved across all evaluation domains. 446 EPAs were completed, with significant variation between trainees (281 vs. 165 EPAs completed; 162 vs. 53 passed), reflecting differing levels of engagement and skill acquisition. EPA pass rates ranged widely, from 81.7% for skin graft to 43.5% for Caesarean section and 22.0% for laparotomy. Competency-based education tools were feasibly implemented despite infrastructure constraints. KIIs highlighted common training challenges-including case mix, service to education ratios, and trainer variability-as well as opportunities for refinement. CONCLUSION: VitalSurg demonstrates the feasibility of embedding task-shifting surgical training into humanitarian clinical activities. This model offers a promising, scalable strategy to expand access to safe surgical care in conflict-affected settings.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".