Understanding the Complexity of Diabetes and Urinary Incontinence in Older Adults Receiving Home Care Services: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
Urinary incontinence (UI) is a very prevalent problem for older adults with type 2 diabetes mellitus (T2DM) receiving home care services. However, the complexities of living with UI and T2DM and providing nursing care for older adults in the home care context have received very little research attention. This article describes a protocol for a convergent, mixed methods research design study to address this knowledge and practice gap. The objectives of the proposed study are to (a) determine the prevalence and correlates of UI in older adults living with T2DM receiving home care services, (b) explore the experiences of older adults living with T2DM and UI receiving home care services, (c) explore the experiences of home care nurses caring for this population, and (d) converge the multiple data sources to provide a deeper understanding of the problem and implications for provision of home care services to this population. The research methods are informed by a model of clinical complexity. The quantitative strand will involve descriptive and inferential analysis of standardized home care assessment data (interRAI) to determine the prevalence and correlates of UI in older adults with T2DM receiving home care services. Interpretive description methods will be used in the qualitative strand, and interviews will be conducted with older adults and home care nurses. The findings will be converged to inform a better understanding of the complexity of living with these chronic conditions for older adults receiving home care services and generate implications for complex intervention development. This protocol highlights methodological insights in utilizing mixed methods research to inform intervention design.
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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 | 0.088 | 0.093 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.054 | 0.010 |
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".