Leaders’ experiences of embedding a simulation-based education programme in a teaching hospital: an interview study informed by normalisation process theory
Bibliographic record
Abstract
There is limited research on the experiences of people in working to embed, integrate and sustain simulation programmes. This interview-based study explored leaders' experiences of normalising a simulation-based education programme in a teaching hospital. Fourteen known simulation leaders across Australia and North America were interviewed. Semi-structured interviews were analysed using reflexive thematic analysis sensitised by normalisation process theory, an implementation science theory which defines 'normal' as something being embedded, integrated and sustained. We used a combined social and experiential constructivist approach. Four themes were generated from the data: (1) Leadership, (2) business startup mindset, (3) poor understanding of simulation undermines normalisation and (4) tension of competing objectives. These themes were interlinked and represented how leaders experienced the process of normalising simulation. There was a focus on the relationships that influence decision-making of simulation leaders and organisational buy-in, such that what started as a discrete programme becomes part of normal hospital operations. The discourse of 'survival' was strong, and this indicated that simulation being normal or embedded and sustained was still more a goal than a reality. The concept of being like a 'business startup' was regarded as significant as was the feature of leadership and how simulation leaders influenced organisational change. Participants spoke of trying to normalise simulation for patient safety, but there was also a strong sense that they needed to be agile and innovative and that this status is implied when simulation is not yet 'normal'. Leadership, change management and entrepreneurship in addition to implementation science may all contribute towards understanding how to embed, integrate and sustain simulation in teaching hospitals without losing responsiveness. Further research on how all stakeholders view simulation as a normal part of a teaching hospital is warranted, including simulation participants, quality and safety teams and hospital executives. This study has highlighted that a shared understanding of the purpose and breadth of simulation is a prerequisite for embedding and sustaining simulation. An approach of marketing simulation beyond simulation-based education as a patient safety and systems improvement mindset, not just a technique nor technology, may assist towards simulation being sustainably embedded within teaching hospitals.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".