Urinary Incontinence After Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Urinary incontinence (UI) is a common and distressing problem after stroke. Although there is evidence of new, effective UI poststroke rehabilitation intervention, it is unknown whether occupational therapists (OTs)' and physical therapists (PTs)' actual practices reflect best practices. We sought to determine the extent to which OTs and PTs identify, assess, and treat UI after stroke and to identify personal and organizational predictors of UI problem identification, best-practice assessment, and intervention. METHODS: Six hundred sixty-three OTs (93% participation rate) and 656 PTs (87% participation rate) working in stroke rehabilitation in Canada were randomly selected and interviewed with a telephone-administered questionnaire. Each responded to a series of open-ended questions related to a generated case (vignette) of a typical client with stroke who was experiencing UI. RESULTS: Only 39% of OTs and 41% of PTs identified UI after stroke as a problem. Fewer than 20% of OTs and 15% of PTs used best-practice assessments, and only 2% of OTs and 3% of PTs used best-practice interventions. Working in Ontario, having allocated learning time, and doing university teaching were among the variables explaining between 6% and 9% of the variability in UI identification and assessment. CONCLUSIONS: Canadian OTs and PTs do not routinely identify poststroke UI as a problem, and best-practice assessments and interventions are underused.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".