Low-Cost Simulation to Teach Anesthetists’ Non-Technical Skills in Rwanda
Bibliographic record
Abstract
BACKGROUND: Safe anesthesia care is challenging in developing countries where there are shortages of personnel, drugs, equipment, and training. Anesthetists' Non-technical Skills (ANTS)-task management, team working, situation awareness, and decision making-are difficult to practice well in this context. Cesarean delivery is the most common surgical procedure in sub-Saharan Africa. This pilot study investigates whether a low-cost simulation model, with good psychological fidelity, can be used effectively to teach ANTS during cesarean delivery in Rwanda. METHODS: Study participants were anesthesia providers working in a tertiary referral hospital in Rwanda. Baseline observations were conducted for 20 anesthesia providers during cesarean delivery using the established ANTS framework. After the first observation set was complete, participants were randomly assigned to either simulation intervention or control groups. The simulation intervention group underwent ANTS training using low-cost high psychological fidelity simulation with debriefing. No training was offered to the control group. Postintervention observations were then conducted in the same manner as the baseline observations. RESULTS: The primary outcome was the overall ANTS score (maximum, 16). The median (range) ANTS score of the simulation group was 13.5 (11-16). The ANTS score of the control group was 8 (8-9), with a statistically significant difference (P = .002). Simulation participants showed statistically significant improvement in subcategories and in the overall ANTS score compared with ANTS score before simulation exposure. CONCLUSIONS: Rwandan anesthesia providers show improvement in ANTS practice during cesarean delivery after 1 teaching session using a low-cost high psychological fidelity simulation model with debriefing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".