Validation of the COVID-19 Digital Health Literacy Instrument in the Italian Language: A Cross-Sectional Study of Italian University Students

Research output: Journal contributionsJournal articlesResearchpeer-review

Authors

  • Chiara Lorini
  • Veronica Velasco
  • Guglielmo Bonaccorsi
  • Kevin Dadaczynski
  • Orkan Okan
  • Patrizio Zanobini
  • Luca P. Vecchio
The Coronavirus Disease 19 (COVID-19) pandemic and the associated “infodemic” have shown the importance of surveillance and promotion of health literacy, especially for young adults such as university students who use digital media to a very high degree. This study aimed to assess the validity and reliability of the Italian version of the COVID-19 adapted version of the Digital Health Literacy Instrument (DHLI). This cross-sectional study is part of the COVID-19 University Students Survey involving 3985 students from two Italian universities. First, item analysis and internal consistency were assessed. Then, Principal Component Analysis (PCA) and Confirmatory Factor Analyses (CFA) were performed comparing different models. The Italian DHLI showed good psychometric characteristics. The protecting privacy subscale was excluded, given the criticalities presented in the validation process. CFA confirmed the four-factor structure, also including a high-order factor. This result allows using the scale to measure a global level of digital health literacy and consider its levels separately for each construct component: searching the web for information, evaluating reliability, determining personal relevance, and adding self-generated content.
Original languageEnglish
Article number6247
JournalInternational Journal of Environmental Research and Public Health
Volume19
Issue number10
Number of pages17
ISSN1661-7827
DOIs
Publication statusPublished - 20.05.2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

    Research areas

  • digital health literacy, COVID-19, university students, infodemic, measurement, scale validation
  • Health sciences

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