Development of quality outcome indicators to improve the quality of urinary and faecal continence care
Bibliographic record
Abstract
INTRODUCTION AND HYPOTHESIS: Despite the range of treatment options available, relatively few people with incontinence find a total cure. The importance of daily management with toileting and containment cannot be underestimated. To our knowledge, there are no outcome measures to benchmark good care. The aim of this study was to create a set of key performance indicators (KPIs) to measure outcomes for toileting and containment. METHODS: An expert panel (EP) defined a set of KPIs using evidence from a scoping review, stakeholder engagement, and expert consensus. Peer reviewed articles, high-quality grey literature and international and national standards were reviewed to identify existing measures for management. These findings were augmented by an exercise involving patients, caregivers, nurses, clinicians, payers, policy makers and care providers to prioritise the findings and identify additional areas of interest. RESULTS: The final set of 14 KPIs includes quality indicators of process and outcome for those managed with a toileting and containment strategy and is relevant for both care-independent and -dependent persons. Rates of assessment, days waiting for specialist assessment, rates of return to work and those rating their quality of life as good or acceptable are captured. An indicator of well-being for caregivers and the economic costs of poor care are also defined. CONCLUSIONS: The set of KPIs to measure outcomes from toileting and containment strategies describes the components of each to encourage integration into existing quality frameworks. Each KPI has been refined and detailed to encourage this. If implemented, resulting benchmarking data will facilitate care quality improvement and inform value-based care procurement and provision of toileting and containment strategies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".