Use of Simulation Learning Experiences in Physical Therapy Entry-to-Practice Curricula: A Systematic Review
Bibliographic record
Abstract
PURPOSE: To review the literature on simulation-based learning experiences and to examine their potential to have a positive impact on physiotherapy (PT) learners' knowledge, skills, and attitudes in entry-to-practice curricula. METHOD: A systematic literature search was conducted in the MEDLINE, CINAHL, Embase Classic+Embase, Scopus, and Web of Science databases, using keywords such as physical therapy, simulation, education, and students. RESULTS: A total of 820 abstracts were screened, and 23 articles were included in the systematic review. While there were few randomized controlled trials with validated outcome measures, some discoveries about simulation can positively affect the design of the PT entry-to-practice curricula. Using simulators to provide specific output feedback can help students learn specific skills. Computer simulations can also augment students' learning experience. Human simulation experiences in managing the acute patient in the ICU are well received by students, positively influence their confidence, and decrease their anxiety. There is evidence that simulated learning environments can replace a portion of a full-time 4-week clinical rotation without impairing learning. CONCLUSIONS: Simulation-based learning activities are being effectively incorporated into PT curricula. More rigorously designed experimental studies that include a cost-benefit analysis are necessary to help curriculum developers make informed choices in curriculum design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".