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Cited by 17Health Literacy and Information AccessibilityDigital literacy in educationHealth disparities and outcomes

Association between social capital, mental health, and digital health literacy among the university students in China: A multigroup analysis based on major difference

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Zhao, Jiajia, Nie, Limei, Pan, Lutong, Pang, Mingli, Wang, Jieru, Zhou, Yue, Chen, Rui, Liu, Hui +4 more·2024·BMC Public Health
DOI 10.1186/s12889-024-19672-7PMC PMC11321090
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Abstract

Background: This study aimed to clarify medical-nonmedical difference on the relationship between social capital, mental health and digital health literacy of university students in China, and furtherly provide evidence-based suggestions on the improvement of the digital health literacy for the university students. Methods: The snowball sampling method was used to collect data from the university students (including medical students and nonmedical students) through online questionnaires, and finally 1472 university students were included for the data analysis, of whom, 665 (45.18%) were medical students, 807 (54.82%) were nonmedical students; 462 (31.39%) were male, 1010 (68.61%) were female. Mean value of the age was 21.34 ± 2.33 for medical students vs. 20.96 ± 2.16 for nonmedical students. Descriptive analysis, chi-square test analysis, one-way Analysis of Variance (conducted by SPSS) and structural equation modeling (conducted by AMOS) were employed to explore the difference on the relationship between social capital, mental health and digital health literacy between the medical students and nonmedical students. Results: The mean value of the digital health literacy was 36.27 (37.33 for medical students vs. 35.39 for nonmedical students). The SEM analysis showed that there was a statistically positive correlation between social capital and digital health literacy (stronger among the nonmedical students (0.317) than medical students (0.184)). Mental health had a statistically positive impact on the digital health literacy among medical students (0.242), but statistically significant correlation was not observed in nonmedical students (0.017). Social capital was negatively correlated with the mental health for both medical students and NMS (stronger among the nonmedical students (0.366) than medical students (0.255)). And the fitness indices of SEM were same between medical students and nonmedical students (GFI = 0.911, AGFI = 0.859, CFI = 0.922, RMSEA = 0.074). Conclusion: The digital health literacy of the university student was relatively high. Both social capital and mental health could exert a positive effect on digital health literacy, while social capital was found to be positively associated with mental health. Statistical difference was found between medical students and nonmedical students on the above correlations. Implications were given on the improvement of the digital health literacy among university students in China.

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  • Participants majoring in health-related fields were excluded because pursuing health-related careers would affect health literacy and health information-seeking behaviors.

    “The screening questions were designed to include only undergraduate students and to exclude those majoring in health-related fields, such as medicine, nursing, and social work, given that pursuing health-related careers would affect health literacy and health information-seeking behaviors (Frings et al., 2022; Zhao et al., 2024).”

    From Digital health literacy and trust in health information sources: A comparative study of university students in Japan, the United States, and India (2025) · cites this paper as Zhao et al., 2024

    The Abstract Results report higher digital health literacy scores for medical than nonmedical students, and the Discussion (Mental health and DHL) describes a difference in how students seek health information. These passages support the concern that the groups may differ, but the paper does not establish that pursuing a health-related career causes those differences. The screening decision belongs to the citing authors, not Zhao et al. (LLM, conf=0.84)

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Authors
Zhao, J., Nie, L., Pan, L., Pang, M., Wang, J., Zhou, Y., Chen, R., Liu, H., Xu, X., Zhou, C., Li, S., & Kong, F.
Affiliations
  • Shandong University (CN)
  • New York University (US)
Corresponding author
Kong, Fanlei
ORCID
Venue
BMC Public Health
Publisher
Springer Science and Business Media LLC
Published
2024-08-13
Volume
24
Issue
1
Pages
2193-2193
ISSN
1471-2458 (ISSN-L)
DOI
10.1186/s12889-024-19672-7
PMC
PMC11321090
OpenAlex
W4401536306
Cited by
17
References
4
Citations by year
2026: 8 · 2025: 9
FWCI
8.09
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Public Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
National Natural Science Foundation of China (71804094)
Topics
  • Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
  • Physical Sciences › Computer Science › Information Systems › Digital literacy in education
  • Social Sciences › Health › Health disparities and outcomes
Keywords
social capital, mental health, digital health literacy, university students, medical students, major differences, health information seeking, health data sharing, health information evaluation
MeSH terms
Adolescent, Adult, China, Female, Humans, Male, Mental Health, Surveys and Questionnaires, Students, Students, Medical, Universities, Multilevel Analysis, Young Adult, Health Literacy, Social Capital

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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