Perceived Digital Counselling Services and Mental Health among Chinese University Students: A Cross-Sectional Study
Downloads
Objective: This study examined gender and academic-year differences in perceived digital counselling services and mental health symptoms among Chinese university students and investigated their association.
Methods and Materials: A cross-sectional online survey was conducted in November 2025 among 1,565 undergraduates from 12 Chinese public universities. Participants were recruited through convenience sampling and had previously accessed digital counselling services. Data were collected using a 14-item digital counselling services questionnaire and a modified, reverse-scored DASS-21-based symptom measure. Independent-samples t tests, one-way ANOVA, Pearson correlation, and multiple linear regression were performed.
Findings: Female students reported higher digital counselling service scores (4.166 vs. 4.027; t= 6.766, p< .001, d= .341) and fewer mental health symptoms (4.395 vs. 4.318; t= 4.992, p< .001, d= .252) than males. Academic-year differences were significant for service perceptions (F= 11.146, p < .001, η² = .021) and mental health symptoms (F= 37.294, p< .001, η²= .067). Service perceptions correlated positively with reverse-scored mental health scores (r= .450, p< .001). This association remained significant after adjustment for gender and academic year (β= .455, p< .001), with the regression model explaining 32.5% of the variance (R²= .325).
Conclusion: More favorable perceptions of digital counselling services were associated with fewer self-reported mental health symptoms. Causal or therapeutic effects cannot be inferred.
Downloads
Alojail, M. (2024). UTAUT model for digital mental health interventions: Factors influencing user adoption. International Journal of Advanced Computer Science and Applications, 15(8), 602–610. https://doi.org/10.14569/IJACSA.2024.0150860
Anteneh, D. S., Kelkay, J. M., Wubneh, H. D., Beshir, M. A., & Gashu, K. D. (2025). Intention to use digital-based psychosocial counseling and its predictors among students in University of Gondar, Northwest Ethiopia, 2023: Using modified unified theory of acceptance and use of technology model. SAGE Open Medicine, 13, Article 20503121241307136. https://doi.org/10.1177/20503121241307136
Chen, J., Liu, Y., Zhu, K., Dai, J., & Wang, C. (2024). The development and evolution of the research topic on the mental health of college students: A bibliometric review based on CiteSpace and VOSviewer. Heliyon, 10(8), Article e29477. https://doi.org/10.1016/j.heliyon.2024.e29477
D’Adamo, L., Paraboschi, L., Grammer, A. C., Fennig, M., Graham, A. K., Yaeger, L. H., Newman, M. G., Wilfley, D. E., Taylor, C. B., Eisenberg, D., & Fitzsimmons-Craft, E. E. (2023). Reach and uptake of digital mental health interventions based on cognitive-behavioral therapy for college students: A systematic review. Journal of Behavioral and Cognitive Therapy, 33(2), 97–117. https://doi.org/10.1016/j.jbct.2023.05.002
Fan, W., Zhang, H., Lei, P., Tang, Y., Du, J., & Li, J. (2025). Uncovering the complex interactions of mental health symptoms in Chinese college students: Insights from network analysis. BMC Psychology, 13(1), Article 448. https://doi.org/10.1186/s40359-025-02731-y
Feng, T., Wang, B., Mi, M., Ren, L., Wu, L., Wang, H., Liu, X., & Wang, X. (2025). The relationships between mental health and social media addiction, and between academic burnout and social media addiction among Chinese college students: A network analysis. Heliyon, 11(3), Article e41869. https://doi.org/10.1016/j.heliyon.2025.e41869
Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). SAGE Publications. https://uk.sagepub.com/en-gb/eur/discovering-statistics-using-ibm-spss-statistics/book238032
Gu, D., Khan, S., Khan, I. U., Khan, S. U., Xie, Y., Li, X., & Zhang, G. (2021). Assessing the adoption of e-health technology in a developing country: An extension of the UTAUT model. SAGE Open, 11(3), Article 21582440211027565. https://doi.org/10.1177/21582440211027565
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Lewis, C. C., Taba, M., Allen, T. B., Caldwell, P. H. Y., Skinner, S. R., Kang, M., Henderson, H., Bray, L., Borthwick, M., Collin, P., McCaffery, K., & Scott, K. M. (2024). Developing an educational resource aimed at improving adolescent digital health literacy: Using co-design as research methodology. Journal of Medical Internet Research, 26, Article e49453. https://doi.org/10.2196/49453
Li, X., Luo, X., Cox, A., Zhang, Y., & Lu, Y. (2023). The mental health information needs of Chinese university students and their use of online resources: A holistic model. Journal of Documentation, 79(2), 442–467. https://doi.org/10.1108/JD-12-2021-0249
Liu, M., Zhuang, A., Norvilitis, J. M., & Xiao, T. (2024). Usage patterns of short videos and social media among adolescents and psychological health: A latent profile analysis. Computers in Human Behavior, 151, Article 108007. https://doi.org/10.1016/j.chb.2023.108007
Ning, X., Wong, J. P.-H., Huang, S., Fu, Y., Gong, X., Zhang, L., Hilario, C., Fung, K. P.-L., Yu, M., Poon, M. K.-L., Cheng, S., Gao, J., & Jia, C.-X. (2022). Chinese university students’ perspectives on help-seeking and mental health counseling. International Journal of Environmental Research and Public Health, 19(14), Article 8259. https://doi.org/10.3390/ijerph19148259
Qiu, L., Xu, H., Li, Y., Zhao, Y., & Yang, Q. (2024). Gender differences in attitudes towards psychological help-seeking among Chinese medical students: A comparative analysis. BMC Public Health, 24(1), Article 1314. https://doi.org/10.1186/s12889-024-18826-x
Riboldi, I., Calabrese, A., Piacenti, S., Capogrosso, C. A., Paioni, S. L., Bartoli, F., Carrà, G., Armes, J., Taylor, C., & Crocamo, C. (2024). Understanding university students’ perspectives towards digital tools for mental health support: A cross-country study. Clinical Practice & Epidemiology in Mental Health, 20, Article e17450179271467. https://doi.org/10.2174/0117450179271467231231060255
Seyed Alitabar, S. H. (2025). The cultural imprint on the mind–body connection: Rethinking health beyond the biomedical model. International Journal of Body, Mind and Culture, 12(4), 1–4. https://doi.org/10.61838/ijbmc.v12i4.1086
Seyed Alitabar, S. H. (2026). The forgotten body in digital mental health: Why culture, embodiment, and human connection must shape the future of AI-assisted care. International Journal of Body, Mind and Culture, 13(6), Article e2026-1624. https://doi.org/10.61838/ijbmc.v13i6.1624
Sit, H. F., Chen, W., Wu, D., Huang, Y., Xu, D., & Hall, B. J. (2024). Digital mental health: A potential opportunity to improve health equity in China. The Lancet Public Health, 9(12), e1136–e1141. https://doi.org/10.1016/S2468-2667(24)00249-4
Su, J., Wang, Y., Liu, H., Zhang, Z., Wang, Z., & Li, Z. (2025). Investigating the factors influencing users’ adoption of artificial intelligence health assistants based on an extended UTAUT model. Scientific Reports, 15(1), Article 18215. https://doi.org/10.1038/s41598-025-01897-0
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson. https://www.pearson.com/en-gb/subject-catalog/p/using-multivariate-statistics-pearson-new-international-edition/P200000005532/9781292021317
Torous, J., Linardon, J., Goldberg, S. B., Sun, S., Bell, I., Nicholas, J., Hassan, L., Hua, Y., Milton, A., & Firth, J. (2025). The evolving field of digital mental health: Current evidence and implementation issues for smartphone apps, generative artificial intelligence, and virtual reality. World Psychiatry, 24(2), 156–174. https://doi.org/10.1002/wps.21299
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Wang, J., Yuan, G. F., Shi, X., Tang, A., & Shi, W. (2022). Factors influencing attitudes toward cyber-counseling among China’s Generation Z: A structural equation model. Archives of Psychiatric Nursing, 40, 124–131. https://doi.org/10.1016/j.apnu.2022.07.011
Wang, K., Shi, H.-S., Geng, F.-L., Zou, L.-Q., Tan, S.-P., Wang, Y., Neumann, D. L., Shum, D. H. K., & Chan, R. C. K. (2016). Cross-cultural validation of the Depression Anxiety Stress Scale-21 in China. Psychological Assessment, 28(5), e88–e100. https://doi.org/10.1037/pas0000207
Wu, R. Y., Ge, L. F., & Zhong, B. L. (2025). Posttraumatic stress symptoms among Chinese university students during the COVID-19 pandemic: Prevalence, correlates, and mental health help-seeking. World Journal of Psychiatry, 15(1), Article 102012. https://doi.org/10.5498/wjp.v15.i1.102012
Yosep, I., Suryani, S., Mediani, H. S., Mardhiyah, A., & Ibrahim, K. (2024). Types of digital mindfulness: Improving mental health among college students – a scoping review. Journal of Multidisciplinary Healthcare, 17, 43–53. https://doi.org/10.2147/JMDH.S443781
Zhang, H., Mao, Y., Lin, Y., & Zhang, D. (2025). E-mental health in the age of AI: Data safety, privacy regulations and recommendations. Alpha Psychiatry, 26(3), Article 44279. https://doi.org/10.31083/AP44279
Copyright (c) 2026 International Journal of Body, Mind and Culture
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
