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 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 | 0.110 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".