The state of aquatic therapy use for clients with spinal cord injury or disorder: Knowledge and current practice
Bibliographic record
Abstract
CONTEXT/OBJECTIVES: Aquatic therapy (AT) has been reported to be beneficial for individuals with spinal cord injury or disorder (SCI/D); however, AT has also been reported to be underutilized in SCI/D rehabilitation. We aimed to understand the knowledge and current practice of AT for clients with SCI/D by physiotherapists, physiotherapy assistants and kinesiologists across Canada. DESIGN/METHOD: A survey with closed- and open-ended questions was distributed (July-October 2019) to professionals through letters sent by professional associations. Non-parametric analyses were used to compare AT knowledge and practice between AT and non-AT users; content analysis was used to identify the themes from open-ended questions. RESULTS: <0.01). Four themes were identified: 1-Variety of physical and psychosocial benefits of AT for people with SCI/D; 2-Attainment of movement and independence not possible on land; 3-Issues around pool accessibility; and 4-Constraints on AT implementation. CONCLUSIONS: Respondents implemented AT to improve health outcomes for patients with SCI/D, despite facing challenges with pool accessibility and numerous constraints. Respondents who provided AT reported having better knowledge of AT and a supported AT practice in the work environment than respondents not providing AT. This study will inform AT stakeholders and institutions when considering strategies to increase the access to AT after SCI/D.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".