Development and Psychometric Testing of a Knowledge Instrument on Incontinence-Associated Dermatitis for Clinicians: The Know-IAD
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to describe the development and evaluation of the psychometric properties of an instrument used to assess clinician knowledge of Incontinence-associated dermatitis (IAD). DESIGN: The instrument was developed in three phases: Phase 1 involved item development; Phase 2 evaluated content validity of the instrument by surveying clinicians and stakeholders within a single state of Australia and, Phase 3 used a pilot multisite cross-sectional survey design to determine composite reliability and evaluate scores of the knowledge tool. SUBJECTS AND SETTINGS: In Phase 1, the instrument was developed by five persons with clinical and research subject expertise in the area of IAD. In Phase 2, content validity was evaluated by a group of 13 clinicians (nurses, physicians, occupational therapists, dietitians, and physiotherapists) working in acute care across one Australian state, New South Wales, along with two consumer representatives. In Phase 3, clinicians, working across six hospitals in New South Wales and on wards with patients diagnosed with incontinence-associated dermatitis, participated in pilot-testing the instrument. METHODS: During Phase 1, a group of local and international experts developed items for a draft tool based on an international consensus document, our prior research evaluating incontinence-associated dermatitis knowledge, and agreement among an expert panel of clinicians and researchers. Phase 2 used a survey design to determine content validity of the knowledge tool. Specifically, we calculated item- and scale-level content validity ratios and content validity indices for all questions within the draft instrument. Phase 3 comprised pilot-testing of the knowledge tool using a cross-sectional survey. Analysis involved confirmatory factor analysis to confirm the hypothesized model structure of the knowledge tool, as measured by model goodness-of-fit. Composite reliability testing was undertaken to determine the extent of internal consistency between constituent items of each construct. RESULTS: During Phase 1, a draft version of the Barakat-Johnson Incontinence-Associated Dermatitis Knowledge tool (Know-IAD), comprising 19 items and divided into three domains of IAD-related knowledge: 1) Etiology and Risk, 2) Classification and Diagnosis, and 3) Prevention and Management was developed. In Phase 2, 18 of the 19 items demonstrated high scale content validity ratios scores on relevance (0.75) and clarity (0.82); and high scale-content validity indices scores on relevance (0.87) and clarity (0.91). In Phase 3, the final 18-item Know-IAD tool demonstrated construct validity by a model goodness-of-fit. Construct validity was excellent for the Etiology and Risk domain (root mean squared error=0.02) and Prevention and Management domain (root mean squared error=0.02); it was good for the Classification and Diagnosis domain (root mean squared error=0.04). Composite reliability (CR) was good in the Etiology and Risk domain (CR=0.76), Prevention and Management domains (CR=0.75), and adequate in the Classification and Diagnosis domain (CR=0.64). Respondents had good understanding of etiology and risk (72.6% correct responses); fairly good understanding of prevention and management of IAD (64.0% correct responses) and moderate understanding of classification and diagnosis (40.2% correct responses). CONCLUSIONS: The Know-IAD demonstrated good psychometric properties and provides preliminary evidence that it can be applied to evaluate clinician knowledge on IAD.
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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 | 0.030 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".