The Chinese Version of the DigiHealthCom (Digital Health Competence) Instrument for Assessing Digital Health Competence of Health Care Professionals: Translation, Adaptation, and Validation Study
Notice bibliographique
Résumé
Background: Digital health competence is increasingly recognized as a core competence for health care professionals. A comprehensive evaluation of knowledge, skills, performance, values, and attitudes necessary to adapt to evolving digital health technologies is essential. DigiHealthCom (Digital Health Competence) is a well-established instrument designed to assess digital health competence across diverse health care professionals. Objective: This study aimed to translate and culturally adapt DigiHealthCom into simplified Chinese (Mandarin) and verify its reliability and validity in assessing digital health competence of Chinese health care professionals. Methods: DigiHealthCom was translated into Chinese following the guideline proposed by its original developers. The cultural adaptation involved expert review and cognitive interviewing. Internal consistency, test-retest reliability, content validity, convergent validity, discriminant validity, and factor structure were examined. Item analysis tested item discrimination, item correlation, and item homogeneity. Internal consistency was assessed using Cronbach α, and test-retest reliability was measured using the intraclass correlation coefficient. Content validity was assessed through both item and scale content validity indices. Convergent validity was measured by the Average Variance Extracted and Composite Reliability, while discriminant validity was measured by the heterotrait-monotrait ratio. A five-dimension model of DigiHealthCom was confirmed using confirmatory factor analysis. Results: The finalized Chinese version of the DigiHealthCom was completed after addressing differences between the back-translations and the original version. No discrepancies affecting item clarity were reported during cognitive interviewing. The validation process involved 398 eligible health care professionals from 36 cities across 15 provinces in China, with 43 participants undergoing a retest after a 2-week interval. Critical ratio values (range 16.05-23.77, P<.001), item-total correlation coefficients (range 0.69-0.89), and Cronbach α if the item deleted (range 0.91-0.96) indicated satisfactory item discrimination, item correlation, and item homogeneity. Cronbach α for dimensions and the scale ranged from 0.94 to 0.98, indicating good internal consistency. The intraclass correlation coefficient was 0.90 (95% CI 0.81-0.95), indicating good test-retest reliability. Item content validity index ranged from 0.82 to 1.00, and the scale content validity index was 0.97, indicating satisfactory content validity. Convergent validity (average variance extracted: 0.60-0.79; composite reliability: 0.94-0.95) and divergent validity (heterotrait-monotrait ratio: 0.72-0.89) were satisfactory. Confirmatory factor analysis confirmed a well-fit five-dimension model (robust chi-square to df ratio=3.10, comparative fit index=0.91, Tucker-Lewis index=0.90, incremental fit index=0.91, root-mean-square error of approximation=0.07, standardized root-mean-square residual=0.05), with each item having a factor loading exceeding 0.40. Conclusions: The Chinese version of DigiHealthCom has been proved to be reliable and valid. It is now available for assessing digital health competence among Chinese health care professionals. This assessment can be used to guide health care policy makers and educators in designing comprehensive and implementable educational programs and interventions.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».