Virtual Reality Instructional Design in Orthopedic Physical Therapy Education: A Randomized Controlled Trial
Bibliographic record
Abstract
Introduction. Effective clinical decision-making (CDM) skills are essential for physical therapist practice. The purpose of this study was to compare the effects of virtual reality (VR) patient simulation with those of a traditional standardized patient simulation on the CDM of student physical therapists (SPTs). Review of Literature. Authentic experiential learning opportunities are needed to promote CDM. The effects of VR simulation on the CDM of SPTs are unknown. Subjects. Fifty-nine first-year SPTs participated in this study. Methods. A randomized controlled trial compared the effects of VR with those of standardized patient simulation on several aspects of CDM in 59 first-year students after an upper extremity orthopedic unit. Perceived CDM abilities and metacognitive awareness were assessed before and after allocated instruction. Diagnostic accuracy and diagnostic efficiency were measured during instruction. Student engagement was assessed immediately after instruction and psychomotor skill was assessed 1 week later. Results. Statistically significant within-group differences in CDM were noted after both VR and standardized patient instruction, but no between-group differences were found. Although effect sizes were considered large with either learning experience, the observed experimental effect was greater after a VR experience. No between-group differences were found between metacognitive awareness, diagnostic accuracy, or psychomotor skill assessment. Diagnostic efficiency was statistically significantly greater in the standardized patient condition, while engagement was significantly greater in the VR condition. Discussion and Conclusion. Measures of perceived CDM improved regardless of instructional method; however, the effect size was greater after VR. These findings reveal 2 effective experiential learning options to promote CDM. These results exemplify the normative trajectory of CDM development and recommendations for differentiated curricular instruction. Although resource intensive initially, VR technology appears capable of advancing CDM skills in an efficient manner that may minimize future cost and the faculty facilitation associated with standardized patient instruction.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".