Assessing healthcare simulation facilitation using a competency‐based tool derived from practice in low‐resource settings
Bibliographic record
Abstract
INTRODUCTION: The worldwide expansion in healthcare simulation training includes accelerated uptake in low-resource settings. Until recently, no framework has specifically delineated the competencies underpinning effective facilitation practice in low-resource settings. We describe the development of the Facilitation Behavioural Assessment Tool for simulation facilitation training and report reliability in scoring facilitation performance. This tool was informed by healthcare simulation facilitation practice in low-resource settings. METHODS: The tool has 32 facilitation competencies, organised across three performance categories (techniques, artistry and values) and a three-point scale is used for scoring. Following a short, self-directed online training module, participants scored three videos that depicted facilitation performance at three levels. Videos were presented in a random order. Intraclass correlations and internal consistency with Cronbach's α were calculated. A random intercepts 3 × 3 linear mixed model assessed discrimination across the three levels of facilitation performance and the influence of previous facilitation on scoring. RESULTS: In total, 104 participants from 29 countries completed rater training and scored at least one video. The inter-rater reliability was 0.73 (95%CI 0.66-0.79) and 0.89 (95%CI 0.85-0.92) for the intraclass correlation coefficient 2 and intraclass correlation coefficient 2k, respectively. Cronbach's α was 0.84 (95%CI 0.79-0.89) for the positive video; 0.84 (95%CI 0.78-0.88) for the mixed video; and 0.91(95%CI 0.87-0.93) for the negative video. Previous simulation facilitation experience did not affect the ability to distinguish between the videos meaningfully, but novice facilitators scored facilitation behaviours higher for mixed and negative videos compared with participants with intermediate and high levels of experience. DISCUSSION: Our study shows that suitable reliability and internal consistency can be achieved when using the Facilitation Behavioural Assessment Tool. We recommend using the tool to support learning conversations for simulation faculty development in low-resource settings.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".