Needs assessment regarding management decision-making for women with stress urinary incontinence
Bibliographic record
Abstract
Objective: We aimed to identify the needs, conflicts, barriers and knowledge gaps associated with decision-making for management options for women with stress urinary incontinence (SUI) and treating healthcare professionals.Methods: Two data collection methods were used: 1. semi-structured interviews with 10 individual patients assessing understanding of SUI management, values, biases, as well as decisional needs, barriers, and facilitators, and 2. focus groups with 13 healthcare professionals to evaluate viewpoints on SUI management decision-needs. Direct content qualitative analysis assessed recurring themes, based on the Ottawa Decision Support Framework and the International Standards for Decision Support.Results: Median age of patients interviewed was 65 ± 18.5 years. 70% previously tried non-invasive procedures (physiotherapy or incontinence pessaries), and 70% tried surgical options, with satisfaction ratings ranging from not satisfied at all, to completely satisfied. 70% reported limited knowledge of possible outcomes of invasive procedures. Financial factors also influenced decision-making in 70%. Fears of undergoing invasive procedures, specifically about possible complications of mid-urethral slings (MUS) secondary to media scrutiny were common. Furthermore, healthcare providers perceived inconsistent knowledge among SUI patients regarding management options, and noted a correlation between patient knowledge, their understanding, and their acceptance of risk and satisfaction. Physicians expressed concerns about the misinformation surrounding MUS, leading to longer consultations to address patients’ concern, and a reluctance from physicians to perform MUS procedures. Providers expressed a need for enhanced education through use of Patient Decision Aids (PDAs).Conclusion: There were significant gaps in the participants' retained knowledge of the available SUI management options. Multiple factors influenced decision-making. The process could potentially be improved by patient decision-aid tools and potentially help minimize risk of patient regret
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".