The “empty Chairs” Approach to Learning: Simulation-Based Train the Trainer Program in Mzuzu, Malawi
Bibliographic record
Abstract
Together, a group of Canadian colleagues from St. John's, Newfoundland, Calgary, Alberta (some via Doha) and London, Ontario introduced the first Train the Trainer in Simulation-Based Learning (TTT-SBL) program in Mzuzu Central Hospital and Mzuzu University in Malawi. The team led by Elaine Sigalet (Doha) and consisting of Ian Wishart (Calgary), Faizal Haji (London) and Adam Dubrowski (St. John's) was invited to Malawi by Norman Lufesi to conduct a two-day TTT-SBL course for facilitators who teach an Emergency Triage, Assessment and Treatment (ETAT) plus Trauma course. The following technical report describes this course. All trainees-facilitators who took part in the first iteration of the TTT-SBL course were asked to participate in teaching an ETAT course and modify it to include elements of simulation. The new format of ETAT resulted in a reduction of time necessary to conduct the course from four days (based on historical data) to 2.5 days.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".