Impact of Simulated Patients on Physiotherapy Students’ Skill Performance in Cardiorespiratory Practice Classes: A Pilot Study
Bibliographic record
Abstract
Purpose: To date, no evidence exists that high-fidelity simulation improves skill development among physiotherapy students in the university setting. With pressures to reduce costs and maintain or improve the quality of the learning experience, and with pressures on clinical placement, it is essential to investigate methods that might improve students’ skill performance before they undertake clinical practice. Our study set out to investigate (1) the impact of using simulated patients (SPs) in a practical class on physiotherapy students’ skill acquisition and (2) the students’ reflections on the intervention. Method: We devised a pilot study using a single-centre randomized controlled trial. A total of 28 undergraduate physiotherapy students, matched using previous practical examination grades, undertook a 2-hour practical class in which they practised their core cardiorespiratory skills. Pre-session resources were identical. The control group practised on peers; the intervention group practised on SPs. The students’ skill performance was assessed 2 weeks later using the Mini-Clinical Evaluation Exercise (MiniCEX), including gathering qualitative data from the students’ reflections. Twenty-eight students undertook the practical class and subsequent MiniCEX assessment. Results: A statistically significant difference was found for all aspects of the MiniCEX except medical interview ( p = 0.07) and physical interview ( p = 0.69), and a large effect size was found for all areas except physical interview (0.154) and medical interview (0.378). The students’ reflections focused on three key themes: behaviours and attitudes, teaching the active cycle of breathing technique, and feedback. Conclusions: Our findings suggest that interacting with SPs improves student skill performance, but further research using a larger sample size and an outcome measure validated for this population is required to confirm this.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".