Assessing the stigma content of urinary incontinence intervention outcome measures
Bibliographic record
Abstract
The goal of this narrative review is to evaluate the efficacy of available questionnaires for assessing the outcomes of "continence difficulty" interventions and to assess the selected questionnaires concerning aspects of stigmatization. The literature was searched for research related to urinary incontinence, as well as questionnaires and rating scale outcome measurement tools. The following sources were searched: Cochrane Library, EMBASE, Medline, and PubMed. The following keywords were used separately or in combination: "Urinary incontinence," "therapy," "treatment outcome," "patient satisfaction," "quality of life," "systematic reviews," "aged 65+ years," and "questionnaire." The search yielded 194 references, of which 11 questionnaires fit the inclusion criteria; 6 of the 11 questionnaires did not have any stigma content and the content regarding stigma that was identified in the other five was very limited. A representative model of how stigma impacts continence difficulty interventions was proposed. While the 11 incontinence specific measurement tools that were assessed were well researched and designed specifically to measure the outcomes of incontinence interventions, they have not been used consistently or extensively and none of the measures thoroughly assess stigma. Further studies are required to examine how the stigma associated with continence difficulty impacts upon health care interventions.
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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 | 0.016 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".