Pretraining Experience and Structure of Surgical Training at a Sub‐Saharan African University
Bibliographic record
Abstract
BACKGROUND: The common goal of surgical training is to provide effective, well-rounded surgeons who are capable of providing a safe and competent service that is relevant to the society within which they work. In recent years, the surgical workforce crisis has gained greater attention as a component of the global human resources in health problems in low- and middle-income countries. The purpose of this study was to: (1) describe the models for specialist surgical training in Uganda; (2) evaluate the pretraining experience of surgical trainees; (3) explore training models in the United States and Canada and areas of possible further inquiry and intervention for capacity-building efforts in surgery and perioperative care. METHODS: This was a cross-sectional descriptive study conducted at Makerere University, College of Health Sciences during 2011-2012. Participants were current and recently graduated surgical residents. Data were collected using a pretested structured questionnaire and were entered and analyzed using an excel Microsoft spread sheet. The Makerere University, College of Health Sciences Institutional Review Board approved the study. RESULTS: Of the 35 potential participants, 23 returned the questionnaires (65 %). Mean age of participants was 29 years with a male/female ratio of 3:1. All worked predominantly in general district hospitals. Pretraining procedures performed numbered 2,125 per participant, which is twice that done by their US and Canadian counterparts during their entire 5-year training period. CONCLUSIONS: A rich pretraining experience exists in East Africa. This should be taken advantage of to enhance surgical specialist training at the institution and regional level.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".