The Psychometric Testing of a Urinary Incontinence Nursing Assessment Instrument
Bibliographic record
Abstract
OBJECTIVE: This study was conducted to examine the psychometric properties of the Capital Health Authority (CHA) Screening Tool. DESIGN: A correlational design was used to evaluate the interrater reliability and concurrent validity of the CHA Tool. Alpha coefficients were calculated as measures of internal consistency. SETTING AND SUBJECTS: Psychometric testing employed convenience sampling to select 12 nurses who work in homecare or geriatric assessment unit settings, and 23 homecare recipients who were experiencing incontinence. INSTRUMENTS: The Stress Urge Incontinence Instrument and Urodynamic Patient Questionnaire were the criterion measures for concurrent validity of the CHA Assessment Tool. METHODS: Interrater reliability testing involved examination of nurse participants' ratings of the same 4 videotaped scenarios of a client reporting differential symptoms of incontinence. Concurrent validity was estimated after the administration of the CHA Tool and the Stress Urge Incontinence Instrument and Urodynamic Patient Questionnaire to homecare clients by the researcher. RESULTS: Correlational analyses revealed good validity and reliability of the urge (r = .81, p < .01, Cronbach's alpha = .83) and stress subscales (r = .78, p < .01, Cronbach's alpha = .66) of the CHA Tool. CONCLUSIONS: This research provides support for a short assessment tool that will benefit individuals experiencing urinary incontinence, their caregivers, and society through improved continence management. The appropriate management of urinary incontinence symptoms could prevent long-term care admissions.
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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 | 0.020 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".