Facilitating in-situ simulations in an acute care environment: A qualitative study
Bibliographic record
Abstract
Background and objective: Clinical Educators frequently use in-situ simulation-based experiences (SBE) to improve the skill and competency of healthcare professionals. The aim of the experience is to improve the quality of patient care and, ultimately, patient outcomes. The facilitator plays a key role in the in-situ SBE as they provide structure, guidance, and support, to help learners achieve the educational outcomes. However, they often face barriers concerning preparation for their role, time release from clinical duties, time to facilitate an effective debrief, and space constraints. The aim of this research was to gain insights into the opportunities and barriers educators face when facilitating in-situ simulations.Methods: A qualitative descriptive design utilising semi-structured interviews with twelve clinical educators who had facilitated in-situ SBE's in the acute care environment within a hospital facility. Interview data was analysed utilising a general inductive approach to determine themes.Results: The facilitators valued in-situ SBE as a teaching and learning strategy however they faced challenges related to time constraints, resourcing, ‘buy in’ and competing priorities for themselves and the learners.Conclusions: Sustaining an in-situ SBE programme long term requires a departmental culture that normalises SBE as routine practice, a simulation design appropriate to the in-situ environment, and opportunities to engage in a community of practice.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".