Development and Psychometric Testing of a Knowledge Instrument on Incontinence-Associated Dermatitis for Clinicians: The Know-IAD
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».