Designing a Contextually Appropriate Surgical Training Program in Low‐resource Settings: The Botswana Experience
Bibliographic record
Abstract
BACKGROUND: The global burden of surgical disease and severe shortage of trained surgeons around the world are now widely recognized. The greatest challenge in improving access to surgical care lies in sub-Saharan Africa, where the number of surgeons per population is lowest. One part of the solution may be to create programs to train surgeons locally. We present our experience with an approach to designing a contextually appropriate surgical curriculum in Botswana. METHODS: Surgical logbooks from the largest tertiary care center in Botswana, dating from 2004 through 2010, were analyzed to yield total case numbers within clearly defined categories. Case numbers and local surgical opinion were combined to design a contextually relevant curriculum, with the Surgical Council on Resident Education curriculum as a template. RESULTS: Logbook analysis revealed that general surgeons in Botswana manage burns and perform a large number of skin grafts and extremity amputations. However, they perform few colonoscopies and complex laparoscopic procedures. The new curriculum included greater emphasis on surgical subspecialty procedures and surgical management of locally relevant conditions, such as the complications of infectious diseases. Less emphasis was placed on management of uncommon conditions such as inflammatory bowel disease. CONCLUSIONS: There are important differences in the scope of general surgery and the knowledge and skills required by general surgeons in Botswana compared with their North American counterparts. We present a simple and inexpensive approach that could serve as a potential model for designing contextually relevant surgical training programs in other low-resource settings.
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How this classification was reachedexpand
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".