Developing and Validating a Competency Framework for Non-clinical Simulation Operations Specialists
Bibliographic record
Abstract
BACKGROUND: Simulation-based education (SBE) is essential for developing and maintaining clinical skills, yet its effectiveness is partially contingent on simulation operations specialists (SOS) who provide technical, pedagogical, and safety support. Traditionally, SOS roles have been filled by clinicians, but healthcare workforce shortages have prompted simulation centres to rely on informal, on-the-job pathways to train non-clinicians as SOS. This approach has raised concerns regarding workforce readiness and highlights the absence of structured training pathways. To address this gap, we developed and validated a competency framework explicitly tailored to entry-level, non-clinical SOS to inform the development of structured training pathways. METHODS: A mixed-methods design guided by participatory action research (PAR) was used to guide this work. This study followed Batt et al.'s six-step model to develop and validate the competency framework. Methods included a narrative review, artificial intelligence (AI)-supported competency generation, semi-structured interviews, a card-sorting exercise, survey-based validation, and focus groups. Results: This study produced a validated competency framework for non-clinical SOS training consisting of 36 competencies across three technical pillars: (i) Simulation Technology (SIMTECH); (ii) Educational Principles (EDUPRI); and (iii) Safety (SAFE), plus a General Competencies (GEN) pillar aligned with transferable knowledge, skills, and attitudes (KSAs). Conclusion: This study provides the first validated competency framework tailored for entry-level, non-clinical SOS, grounded in both theory and real-world perspectives. The final framework offers a foundation for curriculum developers, employers, and certification bodies, and informs the development of accessible training pathways for non-clinicians entering the simulation operations field.
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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 | 0.056 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".