A qualitative pilot study exploring clients’ and health-care professionals’ experiences with aquatic therapy post-stroke in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Aquatic therapy is beneficial for people post-stroke, as it improves their physical function, well-being, and quality of life. There is a lack of description of users' experiences and perspectives toward aquatic therapy that could elucidate contextual factors for aquatic therapy implementation. OBJECTIVES: To explore participants' experiences with aquatic therapy post-stroke as part of a participatory design project to develop an education tool-kit to address the users' needs for aquatic therapy post-stroke. METHODS: A qualitative descriptive study was employed using a purposive sampling. Letters were sent to stroke and aquatic therapy organizations. Individual interviews were conducted either by phone or Zoom with nine participants in the chronic phase of stroke and 14 health-care professionals. All transcripts were coded and analyzed independently by two researchers. Inductive thematic analysis was used to identify the main themes. RESULTS: = 3). From the interviews, two organizing themes were identified: (1) Importance of aquatic therapy (e.g. experiences, benefits, and program approaches); and (2) Aquatic therapy education (e.g. knowledge gaps, sources of learning and communication). CONCLUSIONS: Health-care professionals and clients reported numerous benefits of aquatic therapy post-stroke including, but not limited to, improvements in mobility, balance, wellbeing, and socialization. Lack of formal and informal education and communication as participants' transition from rehab to community were viewed as barriers to aquatic therapy use post-stroke. Developing education material and communication strategies may improve the uptake of aquatic therapy post-stroke.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".