Technological Solutions for Urinary Continence Care Delivery for Older Adults
Bibliographic record
Abstract
PURPOSE: The aim of this scoping review was to examine available evidence regarding use of technology-based continence care delivery for older adults and to identify gaps in knowledge. METHODS: Scoping review. SEARCH STRATEGY: With the help of a medical librarian, CINAHL, Cochrane Library, EMBASE, MEDLINE, ProQuest, PubMed, SCOPUS, Web of Science, and websites were searched. Search terms included technology, sensors, older adults, urinary incontinence, continence care, nursing homes, long-term care, and continence management. All literature elements except for opinion pieces and case reports written in English within the last 15 years were included. Articles not written in the English language were excluded; our search indicated that less than 6% of returned elements were written in other languages. FINDINGS: After duplications were removed, 2146 potential sources were identified. After exclusions, 19 results were included in the review. Review findings suggest positive effects of technology-based continence care on older adults and those involved in their care such as enhanced delivery of a successful toileting program. Information on potential harms, from either the perspective of care provider or recipient, is limited. It is important that needs of older adults and collaborative efforts are considered in the implementation of technology-based continence care. A paucity of guidelines on the use and adoption of technology-based continence care was found; additional research into uptake and sustainability is needed. CLINICAL IMPLICATIONS: Technological solutions, such as sensors, need to be accurate in the measurement of urine saturation levels and timely in notifying caregivers for effective delivery of continence care. Adverse consequences of incontinence, such as incontinence-associated dermatitis or urinary tract infection, may potentially be reduced or avoided with technology-based continence care delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".