Does a Simulated Patient Experience Improve Physiotherapy Students’ Confidence of Shared Decision-Making? A Mixed Methods Study
Bibliographic record
Abstract
Purpose: Physiotherapy entry-level programmes are designed to equip graduates with the skills required to be autonomous practitioners. Innovative teaching methods, such as role-play simulation, are designed to support students' transition into practice. This study aimed to investigate whether a simulated patient experience could influence student confidence when facilitating behavioural change using a shared decision-making approach. Method: A mixed methods design comprising online pre- and post-surveys of student physiotherapists at one U.K. higher education institution, followed by an invitation to participate in a follow-up semi-structured focus group. Pre- and post-simulation surveys were completed in addition to the Modified Satisfaction with Simulation Experience (MSSE) survey. Likert scale data were treated as numeric variables with the median and Interquartile (IQR) range calculated for combined responses across potential answers. Focus groups included semi-structured questions with thematic analysis generating themes. Results: All 39 respondents “Strongly Agreed” that they were satisfied with the simulated experience, which could be transferred to clinical practice (5, IQR 4–5). The stimulation developed both confidence (5, IQR 4–5) and developed participants’ perception of their shared decision-making skills (4, IQR 4–5). Three key emergent themes from the focus groups included (1) Bridging the gap between clinical practice, (2) Authenticity, and (3) Psychological safety. Conclusions: The simulated role-play patient experience improved the confidence and participants’ perception of their ability to use shared decision-making to facilitate behavioural change. Themes from the semi-structured interviews suggested increased authenticity and psychological safety during the task, which could bridge the gap between theoretical teaching and clinical 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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".