Sim for Life: Foundations—A Simulation Educator Training Course to Improve Debriefing Quality in a Low Resource Setting
Bibliographic record
Abstract
INTRODUCTION: Despite the importance of debriefing, little is known about the effectiveness of training programs designed to teach debriefing skills. In this study, we evaluated the effectiveness of a faculty development program for new simulation educators at Mbarara University of Science and Technology in Uganda, Africa. METHODS: Healthcare professionals were recruited to attend a 2-day simulation educator faculty development course (Sim for Life: Foundations), covering principles of scenario design, scenario execution, prebriefing, and debriefing. Debriefing strategies were contextualized to local culture and focused on debriefing structure, conversational strategies, and learner centeredness. A debriefing worksheet was used to support debriefing practice. Trained simulation educators taught simulation sessions for 12 months. Debriefings were videotaped before and after initial training and before and after 1-day refresher training at 12 months. The quality of debriefing was measured at each time point using the Objective Structured Assessment of Debriefing (OSAD) tool by trained, calibrated, and blinded raters. RESULTS: A total of 13 participants were recruited to the study. The mean (95% confidence interval) OSAD scores pretraining, posttraining, and at 12 months before and after refresher were 18.2 (14.3-22.1), 26.7 (22.8-30.6), 25.5 (21.2-29.9), and 27.0 (22.4-31.6), respectively. There was a significant improvement from pretraining to posttraining (P < 0.001), with no significant decay from posttraining to 12 months (P = 0.54). There was no significant difference in OSAD scores pre- versus post-refresher training at 12 months (P = 0.49). CONCLUSIONS: The Sim for Life Foundations program significantly improves debriefing skills with retention of debriefing skills at 12 months.
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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 | 0.003 | 0.006 |
| 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 | 0.001 | 0.002 |
| 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".