A competency framework for simulation facilitation in low‐resource settings: a modified Delphi study
Bibliographic record
Abstract
BACKGROUND: Skilled facilitators are essential to drive effective simulation training in healthcare. Competency-based frameworks support the development of facilitation skills but, to our knowledge, there are no frameworks that specifically address context-sensitive priorities developed with practitioners working in low-resource settings. METHODS: We aimed to develop a core competency framework for healthcare simulation facilitation in low-resource settings using a modified Delphi process. We drew on the domain expertise of members of the Vital Anaesthesia Simulation Training Community of Practice, with the study guided by a four-member steering group experienced in the conduct of simulation in low-resource settings. In survey round 1, participants (n = 54) were presented with an initial competency set derived from a previous qualitative study and co-created a set of 57 competencies for effective simulation facilitation in low-resource settings. In survey round 2, participants (n = 52) ranked competencies by relevance into three performance categories: techniques; artistry; and values. In survey round 3, participants (n = 50) ranked competencies on their importance. The steering group collated results and presented a draft core competency framework. In survey round 4, participants (n = 50) voted with 98% agreement that this framework represented the most relevant and important competencies for effective facilitation of simulation sessions in low-resource settings. RESULTS: The final 32-item framework encompasses core competencies found in existing standards and includes important new concepts such as demonstration of cultural sensitivity; humility; ability to recognise and respond to potential language barriers; facilitation team collaboration; awareness of logistics; and contingency planning. DISCUSSION: This competency-based framework highlights specific practices required for effective simulation facilitation in low-resource settings. Further work is required to refine and validate this tool to train simulation facilitators to deliver effective training to improve patient safety.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".