Bibliographic record
Abstract
Abstract: Urinary (UI) and fecal incontinence (FI) are troublesome conditions for many in society; both UI and FI increase in prevalence with increasing age. Despite well-recognized effects on health, well-being and quality of life, incontinence is often seen by care providers and payers as a social problem, rather than a health related one. Nurses are in a key position to assist those affected by UI. Nurses have the potential to identify people with incontinence, establish appropriate interventions and provide valuable education to empower patients. Indeed, nurses are ideally placed to perform the initial assessment and management of incontinence, that portion of the care pathway which is crucial, but often poorly done. Unfortunately, this is not always easily implemented; nursing staff have identified environmental barriers, such as lack of time at work, and consider UI a low priority that prevents the facilitation of interventions. This article reviews the evidence on nursing involvement, or lack of it, in continence care and suggests a strategy to improve the situation, involving a complex intervention of knowledge translation. Keywords: nursing, continence, knowledge transfer, continence specialist nurses
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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".