Development and Validation of a Revised Multidimensional Digital Health Literacy Scale: Secondary Analysis Using Cross-Sectional Data from the 2022 Getcheckedonline Community Survey in British Columbia, Canada
Notice bibliographique
Résumé
Background: Digital technologies are reshaping health care, making digital health literacy (DHL) a critical competency for navigating online health information. Although widely conceived and measured as a unidimensional measure of DHL, the literature increasingly supports a multidimensional framing of the eHealth Literacy Scale (eHEALS). Studies propose alternative factor structures that can better inform population-level interventions, but these studies have not accounted for the ordinal nature of eHEALS response data. Objective: This study aimed to identify and validate an alternate multidimensional structure of eHEALS accounting for its ordinal response scale. Methods: Data were drawn from the 2022 GetCheckedOnline community survey of consenting English-speaking British Columbia residents aged ≥16 years who reported sexual activity in the past 12 months. Participants were recruited through geo-targeted digital advertisements, community outreach, and in-person recruitment at public events, and community locations. DHL was measured using eHEALS, with responses collected on a 5-point Likert scale. Descriptive statistics summarized eHEALS responses using means, medians, and IQRs. Exploratory and confirmatory factor analyses were used to assess the scale’s structure using polychoric correlations and standard model fit indices. Reliability and validity were evaluated using polychoric ordinal alpha, average variance extracted, and composite reliability, with missing data addressed via multiple imputation. Results: Overall, 1657 participants met inclusion criteria with a mean age of 33.0 (SD 11.77, 95% CI 32.4-33.6) years. Among these 47.3% (95% CI 44.9%-49.7%) identified as women, 30.4% (95% CI 28.1%-32.6%) identified as racialized minorities, 80.5% (95% CI 78.5%-82.3%) reported easy internet access, and 32.2% (95% CI 30.0%-34.5%) had a bachelor’s degree or higher. Across eHEALS items, median scores were 4.0 (IQR 1.0-2.0) with excellent internal consistency (polychoric ordinal α=.92). Exploratory factor analysis supported a 3-factor solution explaining 65.7% of the variance, demonstrated through confirmatory factor analysis (χ217=71.7, P<.001, root-mean-square error of approximation=0.059, standardized root-mean-square residual=0.026, comparative fit index=0.969, Tucker-Lewis Index=0.948). The final model included Information Navigation (standardized loadings=0.765-0.917), Resource Appraisal (0.825-0.892), and Confidence in Use (0.803 for both items), with composite reliability (0.784-0.900), and average variance extracted (0.503-0.738) supporting construct validity. Conclusions: This study confirms a multidimensional structure of eHEALS, identifying Information Navigation, Resource Appraisal, and Confidence in Use as key dimensions of DHL. This revised model enhances measurement precision, enabling more accurate identification of populations with limited DHL and informing the development of targeted, equity-oriented interventions. Future research should aim to confirm this multidimensional structure in more diverse populations and explore how distinct DHL domains influence access to digital health services in various contexts. Additionally, ongoing scale development must adapt to account for the role of emerging technologies, including artificial intelligence and social media algorithms in health care.
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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 | 0,006 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».