Qualities of Effective Vital Anaesthesia Simulation Training Facilitators Delivering Simulation-Based Education in Resource-Limited Settings
Bibliographic record
Abstract
BACKGROUND: Lack of access to safe and affordable anesthesia and surgical care is a major contributor to avoidable death and disability across the globe. Effective education initiatives are a viable mechanism to address critical skill and process gaps in perioperative teams. Vital Anaesthesia Simulation Training (VAST) aims to overcome barriers limiting widespread application of simulation-based education (SBE) in resource-limited environments, providing immersive, low-cost, multidisciplinary SBE and simulation facilitator training. There is a dearth of knowledge regarding the factors supporting effective simulation facilitation in resource-limited environments. Frameworks evaluating simulation facilitation in high-income countries (HICs) are unlikely to fully assess the range of skills required by simulation facilitators working in resource-limited environments. This study explores the qualities of effective VAST facilitators; knowledge gained will inform the design of a framework for assessing simulation facilitators working in resource-limited contexts and promote more effective simulation faculty development. METHODS: This qualitative study used in-depth interviews to explore VAST facilitators' perspectives on attributes and practices of effective simulation in resource-limited settings. Twenty VAST facilitators were purposively sampled and consented to be interviewed. They represented 6 low- and middle-income countries (LMICs) and 3 HICs. Interviews were conducted using a semistructured interview guide. Data analysis involved open coding to inductively identify themes using labels taken from the words of study participants and those from the relevant literature. RESULTS: Emergent themes centered on 4 categories: Persona, Principles, Performance and Progression. Effective VAST facilitators embody a set of traits, style, and personal attributes (Persona) and adhere to certain Principles to optimize the simulation environment, maximize learning, and enable effective VAST Course delivery. Performance describes specific practices that well-trained facilitators demonstrate while delivering VAST courses. Finally, to advance toward competency, facilitators must seek opportunities for skill Progression.Interwoven across categories was the finding that effective VAST facilitators must be cognizant of how context, culture, and language may impact delivery of SBE. The complexity of VAST Course delivery requires that facilitators have a sensitive approach and be flexible, adaptable, and open-minded. To progress toward competency, facilitators must be open to self-reflection, be mentored, and have opportunities for practice. CONCLUSIONS: The results from this study will help to develop a simulation facilitator evaluation tool that incorporates cultural sensitivity, flexibility, and a participant-focused educational model, with broad relevance across varied resource-limited environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".