Skill Translation Following the Vital Anesthesia Simulation Training Facilitator Course: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Simulation-based education (SBE) is common in resource-rich locations, but barriers exist to widespread implementation in low-resource settings (LRSs). Vital Anesthesia Simulation Training (VAST) was developed to offer low-cost, immersive simulation to teach core clinical practices and nontechnical skills to perioperative health care teams. To promote sustainability, courses in new locations are preceded by the VAST Facilitator Course (VAST FC) to train local faculty. The purpose of this study was to explore the experiences of VAST FC graduates in translating postcourse knowledge and skills into their workplaces. METHODS: This qualitative study used focus group interviews with 24 VAST FC graduates (from 12 low- and middle-income and 12 high-income countries) to explore how they had applied new learning in the workplace. Focus groups were conducted by videoconferencing with data transcribed verbatim. Data were analyzed using inductive thematic analysis. RESULTS: Enabler themes for knowledge and skill translation following facilitator training were (1) the structured debriefing framework, (2) the ability to create a supportive learning environment, and (3) being able to meaningfully discuss nontechnical skills. Two subthemes within the debriefing framework were (1.1) knowledge of conversational techniques and (1.2) having relevance to clinical debriefing. Barrier themes limiting skill application were (1) added time and effort required for comprehensive debriefing, (2) unsupportive workplaces, and (3) lack of opportunities for mentorship and practice postcourse. CONCLUSIONS: Participants found parallels between SBE debriefing conversations, clinical event debriefing, and feedback conversations and were able to apply knowledge and skills in a variety of settings post course. This study supports the relevance of simulation facilitator training for SBE in LRSs.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".