Clinician perspectives on integrating motor learning theory and principles into rehabilitation
Bibliographic record
Abstract
BACKGROUND Following a neurological injury, motor learning (ML) and the accompanying theories and principles provide clinicians with the structure and guidelines required to maximize functional recovery. Unfortunately, evidence has shown that ML is not being used to its full benefit within rehabilitation. Therefore, an assessment of how clinicians are implementing ML within clinical practice, and an exploration of the barriers and facilitators they face, should be conducted. The objectives of this study were to 1) describe how confident clinicians are integrating ML into clinical practice, 2) describe how ML is integrated into clinical practice, and the clinician perceived barriers and facilitators, and 3) start to determine what clinicians believe would be beneficial moving forward to begin overcoming these barriers. METHODS A sequential exploratory mixed methods approach was utilized. Two surveys comprised the quantitative section; one was distributed to a convenience sample of clinicians attending a motor learning workshop (n = 28) and the second was primarily distributed via the Ontario and Canadian Physiotherapy Associations (n = 60). The content of the first survey and the Theoretical Domains Framework were used to inform question development for qualitative interviews (n = 7). RESULTS Quantitative: Surveys highlighted barriers and facilitators to integrating ML into clinical practice, such as time, knowledge and understanding, and patient presentations, identified by previously published literature. The second survey highlighted social support as a facilitator. Based on the scales used in the surveys, clinicians have a mid-range confidence level integrating ML within rehabilitation. Qualitative: While the interviews also highlighted the barriers and facilitators identified by the surveys, they emphasized the nuances of these barriers. How ML is approached within the literature, the realities of clinical practice, and the limited guidance clinicians receive with ML exacerbates the impact of the barriers on their clinical practice. CONCLUSIONS The resulting confidence levels and assessments of how ML theories and principles are integrated into clinical practice shows that there are still gaps in the translation of ML-related knowledge into rehabilitation. A future integrated knowledge translation opportunity should be investigated as a potential method of reducing the impact of these barriers on clinical practice.
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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 | 0.055 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".