Bibliographic record
Abstract
INTRODUCTION: Behavioral strategies such as pelvic floor muscle exercises (PFME), bladder retraining, and dietary modifications are generally considered to be the first line of treatment for urinary incontinence (UI). Yet little is understood about the client's abilities/motivation to manage their UI in the home setting. Self-care, the ability of clients to act on their own behalf to achieve and maintain health, is a fundamental component of these strategies. Despite the frequently chronic nature of UI, there is growing evidence that such maintenance of behavioral therapies is sporadic at best. OBJECTIVE: The purpose of this study was to enhance understanding of self-care strategies that individuals with UI employ, the perceived benefits of these strategies, the factors that influence their self-care choices, and the factors that impede or facilitate maintenance of behavioral therapies. METHOD: In this qualitative descriptive study, individual and focus group interviews with community-dwelling participants were conducted to enhance understanding regarding the participants' management of UI at home and why they maintain certain strategies and not others. Data were collected via loosely constructed individual (n=25) and focus group (n=3) interviews to facilitate open discussion of participants' perceptions. RESULTS: Thirty-eight individuals (33 women and 5 men) participated in the study. Analysis of data resulted in a major category of self-care strategies related to UI that was further subcategorized into factors that facilitated PFME and barriers to PFME performance. Factors that facilitated PFME included: (a) realistic goals and expectation, (b) positive affirmation, (c) follow up, and (d) maintaining an exercise routine. Barriers noted were: (a) insufficient information, (b) characteristics of the exercises, (c) competing interests, (d) financial cost, and (e) minor psychosocial impact. CONCLUSIONS: This study described the self-care strategies that participants with UI had initiated and maintained and additionally explored the perceived facilitators and barriers to self-care choices. Two major themes emerged: (a) self-care efforts were motivated by desire for a normal daily lifestyle and (b) participants were motivated to maintain strategies by the ability to visualize progress and by knowledge that they were progressing. These findings support the need for client-focused teaching that is grounded in the individual's daily realities and goals.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".