Factors Influencing Exam Performance of Surgical Trainees in Sub-Saharan Africa: A Retrospective Analysis of the College of Surgeons in East, Central, and Southern Africa Membership Examination
Bibliographic record
Abstract
INTRODUCTION: The College of Surgeons of East, Central, and Southern Africa (COSECSA) has been expanding surgical training in sub-Saharan Africa to respond to the shortage in the region. However, acquiring surgical skills requires rigorous training, and these skills are repeatedly assessed throughout training. Therefore, understanding the factors influencing these assessments is crucial. Previous research has identified individual characteristics, educational background, curriculum structure and previous exam outcomes to influence performance. However, COSECSA's Membership of the College of Surgeons (MCS) exam has not been investigated for factors influencing performance, which this study aims to investigate. METHODS: Data from MCS trainees who took the exam between 2015 and 2021 were analyzed. Trainee demographics, institutional affiliation, operative experience, and exam performance were considered. Linear regression models were used to analyze the factors related to written and clinical exam performance. RESULTS: Out of 354 trainees, 228 were included in the study. Factors such as training duration, the ratio of emergency surgeries, institutional funding source, and country language were associated with written exam performance. Training duration, funding source, exposure to major surgeries, and the ratio of performing operations were significant factors for the clinical exam. DISCUSSION: Operative experience, institutional affiliation, training duration, and language proficiency influence exam performance. Hospitals funded by faith-based organizations or nongovernmental organizations had trainees with higher scores. Prolonged training did not guarantee improved performance. Lastly, having English as an official language improved written exam scores. Gender and country of training did not significantly impact performance. CONCLUSION: This study highlights the importance of operative experience, institutional affiliation, and language proficiency in the exam performance of surgical trainees in COSECSA. Interventions to enhance surgical training and improve exam outcomes in sub-Saharan Africa should consider these factors. Further research is needed to explore additional outcome measures and gather comprehensive data on trainee and hospital characteristics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".