Identifying Womens' Needs in Making a Treatment Decision for Stress Urinary Incontinence: A Qualitative Study
Bibliographic record
Abstract
Background: Choosing a treatment option for female stress urinary incontinence (SUI) is a preference-sensitive decision. Nowadays, shared decision making (SDM) is the preferred way of decision making. SDM considers the needs patients have regarding the decision-making process. The aim of this study was to identify decisional needs of women who are making a treatment decision for SUI. Materials and Methods: Semistructured interviews were planned with women who had been seeking treatment for SUI. Patients were recruited in two teaching hospitals in the Netherlands. Interviewers used a topic list based on the Ottawa decision support framework. The interviews were transcribed and coded. Themes and subthemes of factors relating to the treatment decision-making process were identified and described. Results: We interviewed a total of 16 women. Four major themes of SUI patients' needs were identified: information on disorder and treatment, SDM, personalized health care, and consideration for social context. Within these themes, specific needs varied between individuals. In addition to the provision of objective information, other important identified needs were subjective, such as acknowledgment of symptoms and feeling understood by a physician. It was important for patients that they had a sufficient amount of time to make their decision. Conclusions: To ensure a good quality treatment decision in female SUI, several topics need to be addressed in an SDM process. The themes of decisional needs identified in this study can help improve the decision-making process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".