Translation of evidence into a self-management tool for use by women with urinary incontinence†
Bibliographic record
Abstract
BACKGROUND: many older women with urinary incontinence remain under-treated. OBJECTIVE: to develop and evaluate an evidence-based self-management urinary incontinence risk factor modification tool for older women. DESIGN: the tool was developed using evidence from a systematic review and input from focus groups. A 6-month prospective cohort study using an interrupted time-series design was conducted to evaluate the tool. SETTING: the tool was developed at the University of Toronto and then evaluated at the Universities of Calgary and Montreal, Canada. SUBJECTS: the tool was developed with the help of focus groups of healthcare professionals and of older incontinent women. The tool was evaluated among 103 incontinent women aged 50 years or older. METHODS: the tool includes six risk factors with modification strategies. The primary outcome was successful tool usage. Secondary outcomes included urinary leakage, change in self-efficacy and quality of life. RESULTS: the tool was used by 95% [95% confidence interval (CI) 88-98] of women at some point. Urinary leakage rates were reduced by an average of 1.4 daily episodes (95% CI 1.0-1.8). Women reported significant improvement in self-efficacy and incontinence-related quality of life. CONCLUSIONS: there appears to be a role for an evidence-based self-management urinary incontinence risk factor modification tool.
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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 | 0.076 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".