Examining the Use of Constraint-Induced Movement Therapy in Canadian Neurological Occupational and Physical Therapy
Bibliographic record
Abstract
PURPOSE: To investigate the use of constraint-induced movement therapy (CIMT) in Canadian neurological occupational and physical therapy. METHOD: An online survey was completed by occupational and physical therapists practising in Canadian adult neurological rehabilitation. We measured participants' practices, perceptions, and opinions in relation to their use of CIMT in clinical practice. RESULTS: A total of 338 surveys were returned for a 13% response rate; 92% of respondents knew of CIMT, and 43% reported using it. The majority (88%) of respondents using CIMT employed a non-traditional protocol. Self-rating of level of CIMT knowledge was found to be a significant predictor of CIMT use (p≤0.001). Commonly identified barriers to use included "patients having cognitive challenges that prohibit use of this treatment" and "lack of knowledge regarding treatment." CONCLUSIONS: Although the majority of respondents knew about CIMT, less than half reported using it. Barriers to CIMT use include lack of knowledge about the treatment and institutional resources to support its use. Identifying and addressing barriers to CIMT use-for example, by using continuing professional education to remediate knowledge gaps or developing new protocols that require fewer institutional resources-can help improve the feasibility of CIMT, and thus promote its clinical application.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".