# Understanding Health Literacy and eHealth Literacy in Nursing Students: A Cross-Sectional Cluster Analysis

**Authors:** Irene Zerilli, Giampiera Bulfone, Donatella Capizzello, Angelo Gambera, Vito Fazzino, Marco Sudano, Antonio Vinci, Fabio Ingravalle, Massimo Maurici

PMC · DOI: 10.3390/nursrep16020052 · Nursing Reports · 2026-02-02

## TL;DR

This study explores health and eHealth literacy levels among nursing students and identifies distinct subgroups with varying literacy profiles.

## Contribution

The study introduces a cluster analysis approach to reveal diverse literacy profiles among nursing students across educational years.

## Key findings

- Four distinct clusters of literacy profiles were identified among nursing students.
- Significant differences in demographic and educational variables were found across clusters.
- The study highlights the need for tailored educational strategies to address varying literacy levels.

## Abstract

Background: Health literacy and eHealth literacy are core competencies for nursing students, yet their distribution across training pathways remains insufficiently explored. Objective: This study aimed to examine HL and eHL levels among nursing students across different years of the educational programme and identify distinct subgroups of students. Methods: A cross-sectional study was conducted among undergraduate nursing students enrolled in all years of a single Italian university programme. Literacy profiles were assessed using validated questionnaires. A Two-Step Cluster Analysis was applied to identify homogeneous literacy profiles. Group differences were examined using appropriate statistical tests. Results: Four distinct clusters were identified, showing heterogeneous patterns of literacy profiles across the training course. Significant differences emerged in demographic and educational variables across clusters. Conclusions: The findings highlight the coexistence of diverse literacy profiles among nursing students and suggest the need for tailored educational strategies. Due to the cross-sectional design, causal inferences cannot be drawn.

## Full-text entities

- **Genes:** LIPC (lipase C, hepatic type) [NCBI Gene 3990] {aka HDLCQ12, HL, HTGL}
- **Diseases:** HL (OMIM:603663), chronic diseases (MESH:D002908), injury to (MESH:D014947), HL (MESH:C538324)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## References

36 references — full list in the complete paper: https://tomesphere.com/paper/PMC12943193/full.md

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Source: https://tomesphere.com/paper/PMC12943193