Managing urinary incontinence in older people in hospital: a best practice implementation project
Bibliographic record
Abstract
OBJECTIVES: This project aimed to improve care in managing urinary incontinence in older patients admitted to a medium-to-long-stay hospital by developing and implementing strategies to improve the compliance with best practice in managing urinary incontinence and decrease its prevalence. INTRODUCTION: Urinary incontinence (UI) is a major problem in hospitalized older people and is of great significance to public health. The application of evidence-based recommendations for this problem could be expected to improve the quality of care. METHODS: The project used the Joanna Briggs Institute's Practical Application of Clinical Evidence System and Getting Research into Practice audit tools for promoting change in healthcare practice. Participants were evaluated at baseline and at two follow-ups at three and six months after key strategies had been implemented. The location of this implementation project was the functional rehabilitation ward of a medium-to-long-stay Spanish hospital. RESULTS: In baseline audit there were four process criteria with a high level of compliance: two criteria with 35% and 44% respectively and one criterion without compliance. Action was taken to address the four barriers identified, leading to an increase in all cases except one, which was related to the characteristics of the patient. Prevalence of urinary incontinence decreased at follow-up. CONCLUSIONS: The development and implementation of strategies improved quality of care. This project obtained positive results in patient health, and the implementation of the strategies used decreased the prevalence of urinary incontinence in patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.071 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".