Efficacy of Surgical Simulation Training in a Low‐Income Country
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: Simulation training has evolved as an important component of postgraduate surgical education and has shown to be effective in teaching procedural skills. Despite potential benefits to low- and middle-income countries (LMIC), simulation training is predominately used in high-income settings. This study evaluates the effectiveness of simulation training in one LMIC (Rwanda). METHODS: Twenty-six postgraduate surgical trainees at the University of Rwanda (Kigali, Rwanda) and Dalhousie University (Halifax, Canada) participated in the study. Participants attended one 3-hour simulation session using a high-fidelity, tissue-based model simulating the creation of an end ileostomy. Each participant was anonymously recorded completing the assigned task at three time points: prior to, immediately following, and 90 days following the simulation training. A single blinded expert reviewer assessed the performance using the Objective Structured Assessment of Technical Skill (OSATS) instrument. RESULTS: The mean OSATS score improvement for participants who completed all the assessments was 6.1 points [95 % Confidence Interval (CI) 2.2-9.9, p = 0.005]. Improvement was sustained over a 90-day period with a mean improvement of 4.1 points between the first and third attempts (95 % CI 0.3-7.9, p = 0.038). Simulation training was effective in both study sites, though most gains occurred with junior-level learners, with a mean improvement of 8.3 points (95 % CI 5.1-11.6, p < 0.001). Significant improvements were not identified for senior-level learners. CONCLUSION: This study supports the benefit for simulation in surgical training in LMICs. Skill improvements were limited to junior-level trainees. This work provides justification for investment in simulation-based curricula in Rwanda and potentially other LMICs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it