Evaluation of an Electronic Monitoring Device for Urinary Incontinence in Elderly Patients in an Acute Care Setting
Bibliographic record
Abstract
AIM: The primary aim of this study was to determine whether the use of habit training with an electronic monitoring device is better than standard habit training in the assessment and management of urinary incontinence in elderly patients in acute care hospitals. The second aim was to describe nurses' perceptions of continence management in acute care settings. DESIGN: A randomized controlled trial was conducted for an 18-month period. SAMPLE AND SETTING: The sample consisted of 41 elderly incontinent patients who resided on the acute care rehabilitation wards of 2 western Australian hospitals. INSTRUMENTS: A continence assessment form was used to document self-reported or carer-reported frequency and severity of incontinence. A continence monitoring chart was used to record voiding patterns and continence outcomes. A semi-structured interview guide was developed to elicit nurses' perceptions of continence management. RESULTS: Findings revealed no significant improvements in the self-reported or carer-reported frequency of incontinence from baseline to follow-up of in-patients and at 1 month after discharge, although there was a trend toward improvement in both the experimental and the control groups at the posttest time points. A significant reduction in self-reported or carer-reported severity of incontinence was demonstrated in the experimental group at 1 month follow-up (P = 5.025).Nurses' perceptions of continence management of elderly patients in acute care settings ranged from positive to extremely negative. Even those with positive perceptions were challenged by problems with device malfunction, perceptions of lack of time, lack of support from other staff, and lack of knowledge about how best to meet the continence management needs of this complex group of patients. CONCLUSION: The potential value of an electronic monitoring device for reducing incontinence could not be adequately assessed because of the small sample size and problems with the device and with implementing the study protocol. Although it was possible to determine a pattern in voiding times using the electronic device, compliance with the toileting regimen was difficult to achieve.Researchers and clinicians planning future studies with this device should be aware of the practical limitations associated with habit-training regimens for elderly patients in the acute care setting.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".