背景:本研究旨在阐明社会资本之间的医学与非医学差异,中国大学生心理健康与数字健康素养,并进一步为提高大学生数字健康素养提供循证建议。
方法:采用滚雪球抽样的方法,通过在线问卷调查的方式收集大学生(包括医学生和非医学生)的数据,最终1472名大学生被纳入数据分析,谁,665名(45.18%)是医学生,非医学生807人(54.82%),男性462人(31.39%),1010例(68.61%)为女性。医学生的年龄平均值为21.34±2.33非医学生为20.96±2.16。描述性分析,卡方检验分析,采用单向方差分析(SPSS)和结构方程模型(AMOS)来探讨社会资本之间关系的差异,医学生与非医学生之间的心理健康和数字健康素养。
结果:数字健康素养的平均值为36.27(医学生为37.33非医学生35.39)。SEM分析表明,社会资本与数字健康素养之间存在统计学正相关(非医学生(0.317)强于医学生(0.184))。心理健康对医学生的数字健康素养有统计学上的积极影响(0.242),但在非医学生中没有观察到统计学显著的相关性(0.017)。医学生和NMS的社会资本与心理健康呈负相关(非医学生(0.366)比医学生(0.255)更强)。医学生与非医学生的SEM适应度指数相同(GFI=0.911,AGFI=0.859,CFI=0.922,RMSEA=0.074)。
结论:大学生的数字健康素养相对较高。社会资本和心理健康都可以对数字健康素养产生积极影响,而社会资本被发现与心理健康呈正相关。在上述相关性上,医学生与非医学生之间存在统计学差异。对提高我国大学生的数字健康素养提出了启示。
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).
CONCLUSIONS: 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.