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. 2024 Aug 13;24(1):2193.
doi: 10.1186/s12889-024-19672-7.

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

Jiajia Zhao  1   2   3 , Limei Nie  1   2   3 , Lutong Pan  1   2   3 , Mingli Pang  1   2   3 , Jieru Wang  1   2   3 , Yue Zhou  4 , Rui Chen  1   2   3 , Hui Liu  1   2   3 , Xixing Xu  1   2   3 , Chengchao Zhou  1   2   3 , Shixue Li  1   2   3 , Fanlei Kong  5   6   7
Affiliations

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

Jiajia Zhao et al. BMC Public Health. .

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.

Keywords: Digital health literacy; Mental health; Social capital; University students.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Conceptual model of the association between social capital and mental health and DHL
Fig. 2
Fig. 2
Structural equation modeling analysis of the association between social capital, mental health and DHL (digital health literacy) of the MS (n = 665). Employing the data, association between social capital, mental health and DHL were analyzed. The arrows indicate the associations and directions between variables. All parameter estimates were statistically significant (P < 0.001). Note: CMIN = Chi Square; df = Degrees of Freedom; GFI = Goodness of Fit Index; AGFI = Adjusted Goodness of Fit Index; CFI = Comparative Fitness Index; RMSEA = Root-mean Square; MS = Medical Students; SRDHL = Self-rated of Digital Health Literacy; TDOHI = Trust Degree of Online Health Information
Fig. 3
Fig. 3
Structural equation modeling analysis of the association between social capital, mental health and DHL (digital health literacy) of the NMS (n = 807). Employing the data, association between social capital, mental health and DHL were analyzed. The arrows indicate the associations and directions between variables. All parameter estimates were statistically significant (P < 0.001), except for the impact of mental health on DHL. Note: CMIN = Chi Square; df = Degrees of Freedom; GFI = Goodness of Fit Index; AGFI = Adjusted Goodness of Fit Index; CFI = Comparative Fitness Index; RMSEA = Root-mean Square; NMS = Nonmedical Students; SRDHL = Self-rated of Digital Health Literacy; TDOHI = Trust Degree of Online Health Information
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