Lessons about Motor Learning: How Is Motor Learning Taught in Physical Therapy Programmes Across Canada?
Bibliographic record
Abstract
Purpose: This article describes the content of and delivery methods for motor learning (ML) education and the attitudes and beliefs of instructors with regard to how ML is taught in Canadian physical therapy (PT) programmes. Method: A qualitative descriptive design was employed, using an online questionnaire and semi-structured telephone interviews. A descriptive content analysis was used to develop codes and themes. An online search of PT programme Web sites was conducted to supplement missing data and collect information from schools that did not participate in the online questionnaire or interview. Results: Eight individuals representing seven schools completed the questionnaire; six of the eight also completed the interview. Responses conveyed the fact that ML content was fairly consistent across schools and was predominantly situated in the neurological curriculum. Schools differed in the delivery methods used for clinical application of ML content. Respondents believed that ML underlies PT practice and should be integrated throughout the programme. Conclusion: Current instruction may deliver adequate ML content but may not provide optimal opportunities to apply ML principles in a clinical context. Continuing education emerged as one suggestion for remediating clinicians' knowledge–practice gap and facilitating student learning on placement. Only half the eligible PT schools participated, and all were English-language programmes; thus, the findings may not be generalizable to all Canadian programmes. Future work should explore how ML can be integrated into the PT curriculum to promote the application of ML principles across different fields. Students' perspectives on their understanding of ML and ML principles and self-efficacy for entry to practice should also be explored.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 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".