Effectiveness of a Geriatric Urinary Incontinence Educational Program for Nursing Staff
Bibliographic record
Abstract
Incontinence is becoming an increasingly widespread phenomenon. Individuals who suffer from incontinence-related problems, as well as their loved ones, must learn to live with daily situations that are often extremely demanding. In recent years, it has become evident that incontinence can be cured, or at least successfully managed. Many nurses, however, lack sufficient knowledge and skills to intervene appropriately in dealing with problems associated with a lack of bladder control. Therefore, the continence team of the Sherbrooke Geriatric University Institute developed a urinary incontinence educational program for nurses who care for older persons with this problem. An assessment of the impact of this program on nurses' knowledge, intervention skills and attitudes was conducted. A quasi-experimental protocol based on single-group with repeated-measures design was used for this study. A convenience sample was made up of 10 staff members at the Sherbrooke Geriatric University Institute. The study demonstrated a significant improvement in participants' knowledge, skills and attitudes (p=0.005, p=0.005 and p=0.017, respectively). Furthermore, the effects of the program in regard to the acquisition of skills and knowledge by nurses were still evident nine weeks after the program. The value of the present study lies in the development and positive evaluation of a new incontinence educational program for nurses based on a problem-solving approach.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".