Harnessing lived experience in health professions simulation-based education: a scoping review
Bibliographic record
Abstract
Simulation is an established pedagogical approach in health professions education, typically led by educators and informed by their clinical expertise. Partnerships between educators and people with lived experience ensures simulation authentically represents the needs of people accessing healthcare. To map available literature on how lived experiences are incorporated into health professions simulation-based education a scoping review was conducted. In April 2024 CINAHL Complete, Scopus, ERIC, Medline, PsycINFO, and ProQuest Dissertations and Theses Global Database were searched. Studies were screened against the inclusion criteria, and data was extracted from 45 studies using a purposively developed and piloted extraction tool, and organised according to four research questions. Medicine and nursing most commonly include lived experiences in simulation-based education and cultural and linguistic diversity is the lived experience most often harnessed. Lived experience involvement across the entire six phases of simulation design and delivery was not common, however active and meaningful involvement was represented at each stage. Lived experience involvement enhances simulation-based education and provides an additional opportunity for people with lived experience to be involved in health professions education. There is an urgent need for guidelines describing how educators can harness lived experiences in simulation-based education. Further research, in partnership with people with lived experience, is required to determine how to more authentically represent lived experience in simulation-based education.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".