Simulation-augmented education in the rehabilitation professions: A scoping review
Bibliographic record
Abstract
Background Simulation-augmented education is playing an increasingly important role in health professions education, yet little is known about the uptake of these interventions in the educational programmes and research for rehabilitation professions. Therefore, it is difficult to determine the benefits of simulation-augmented education interventions, and how such approaches can inform education practice, policy and research. Aims The purpose of this review was to determine what is currently known about the use of simulation in the education of rehabilitation professionals. Methods We conducted a scoping review that included a literature review of published and grey literature, followed by qualitative content analysis of the included references. A program evaluation framework was employed to structure data extraction and analysis. Results Several forms of simulation are commonly used among rehabilitation professions for expertise development, formative and summative evaluations, and to enhance course work and curricula. However, there is a dearth of published literature concerned with the longitudinal impact of simulation-augmented educational strategies, the perspectives of accrediting bodies, and the processes that lead to successful or unsuccessful educational interventions. Conclusions Results provide important future directions for the field of simulation-augmented education, which will optimize their benefits in educational programming and research for the rehabilitation professions.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".