Transforming traditional physiotherapy hands-on skills teaching into video-based learning
Bibliographic record
Abstract
BACKGROUND: Pandemic-induced restrictions forced curriculum transformation from on-site education to virtual learning options. This report describes this transition, the challenge of creating technology-enhanced learning for hands-on psychomotor skills teaching in physiotherapy, and students' evaluations of the new technology-enhanced learning approach in Complex Decongestive Physiotherapy. METHODS: On-site theoretical background lectures were replaced with e-learning sessions. Faculty hands-on skills demonstrations for the entire class were replaced with video-recorded demonstrations. Videos included verbal and written instructions and were complemented with checklists guiding the students, training in pairs, through their learning tasks. A cross-sectional observational survey for teaching quality evaluated this new technology-enhanced learning approach and assessed students' preference for traditional or video-based hands-on skills learning. RESULTS: Survey return rate was > 50% (46 participating students). Teaching quality was rated between 1.5 ± 0.5 and 1.8 ± 0.4 (Likert scale from - 2 to + 2). Most students (66.7%) preferred the new approach. They appreciated for example that videos were available all the time, enabling self-paced learning, providing an equally good view on skills demonstrations, and the convenience to be able to rewind, re-view, and use speed adjustment options. CONCLUSIONS: Students preferred the new video-based learning of skills for Complex Decongestive Physiotherapy. Because in-class live skills demonstrations were omitted, faculty had more time to provide individual feedback and answer questions. The shift from teacher- to student-centered learning enabled students to control their own learning pace. The innovative program was maintained after pandemic-induced restrictions were lifted. The success of this approach should be tested in other physiotherapy settings and different educational institutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".