Addressing the severe shortage of health care providers in Ethiopia: bench model teaching of technical skills
Bibliographic record
Abstract
CONTEXT: There is a severe shortage of health care workers in Ethiopia. This situation must be addressed by the efficient training of mass cohorts of students. OBJECTIVES: This study aimed to demonstrate that bench model training is a feasible approach to teaching surgical skills in Ethiopia. METHODS: A pre-test, simulation-based training intervention and post-test design was used. Two objective structured assessments of technical skills (OSATS) and a bench-top simulation training session were administered at the Black Lion Hospital, Addis Ababa, Ethiopia. Participants included 19 surgical residents who volunteered as trainees. Five surgical faculty members and one senior resident from the Black Lion Hospital, as well as two faculty members from the University of Toronto, participated as trainers and evaluators. The intervention consisted of OSATS tests comprising four stations, covering knot tying, closure of skin laceration, elliptical excision and bowel anastomosis. Tests were separated by 2-hour practice sessions. Main outcome measures included previously validated instruments comprising global rating scales (GRS) and skill-specific checklists (SSC). RESULTS: The measures showed no improvement on knot tying (GRS: P = 0.14; SSC: P = 0.7), marginal improvement on closure of laceration (GRS: P = 0.48; SSC: P = 0.003), and improvements on excision (GRS: P = 0.012; SSC: P = 0.003) and bowel anastomosis (GRS: P < 0.001; SSC: P < 0.001). CONCLUSIONS: The bench models and scoring schemes developed in Toronto, Canada were directly applicable in Addis Ababa, Ethiopia. This approach may prove a feasible, safe and cost-effective method for training a multitude of health care professionals in technical skills and may help to address the human resources deficit in Africa.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".