A protocol for the co-creation and usability/acceptability testing of an evidence-based, patient-centred intervention for self-management of urinary incontinence in older men
Bibliographic record
Abstract
Male urinary incontinence (UI) is most prevalent in older men, with one in three men aged 65 and above having problems maintaining continence. Addressing health inequalities, male-female disparities in continence services, and low health-seeking among men emphasizes the necessity for co-creating an intervention that empowers them to self-manage their UI. We aim to co-create a self-management intervention with an older men and Health care provider (HCP) group and assess its usability and/or acceptability among older men with UI. The intervention mapping (IM) framework, a co-creation strategy, will be used to co-create a self-management tool, followed by usability and/or acceptability testing. The study will be guided by the first four IM steps: the logic model of the problem, the logic model of change, program/intervention design, and program/intervention production, followed by preliminary testing. A participatory group of older men with UI recruited from an existing group of patient partners, and continence care experts will be involved in all steps of the IM process. Usability and/or acceptability testing will be conducted on a sample of 20 users recruited through seniors' associations and retirement living facilities. After accessing the self-management tool for a week, participants will complete a product usability testing scale (aka System Usability Scale-SUS) and/or an acceptability test, depending on the preferred mode(s) of intervention delivery. Data will be analyzed using descriptive statistics. A benchmark overall mean usability score of 70 represents a good/usable product, based on the large database of SUS scores.
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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 | 0.147 | 0.114 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.095 | 0.023 |
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".