- Descriptive statistics of demographics and learning burnout
From the 2214 students who were invited to participate, a total of 683 responses were received giving an overall response rate of 30.8%. Since medical students should study and live in the main campus in their first year, students in our college are mainly sophomores and juniors, accounting for 45.39% and 34.26%. The median age of the students was 20 years (range 17 – 24). Over fifty percent of respondents were female. The numbers (percentages) of municipalities or provincial capitals, prefecture-level cities, county-level cities and town or rural areas were 132(19.33%), 167 (24.45%), 199(29.14%) and 185 (27.09%), respectively. More than three-fifths (63.69%) of the students’ family income in 2019 bellow 14, 150 $. A total of 367 respondents served as class cadres and 388 respondents received scholarships during college. Our results showed that 315 (46.12%) students displayed evidence of learning burnout, with 370 (54.1%) reporting high dejection, 329 (48.1%) reporting high improper behavior, and 295 (43.19%) reporting high reduced personal accomplishment during online study. Table 1 shows the demographic characteristics and learning burnout of the responding students.
Table 1. Demographic Characteristics and learning burnout of Respondents
- Analysis of differences in variables according to demographic and learning characteristics
Table 2 displays differences of the numbers and percentages of learning burnout and its subscales among demographic and online learning feature groups. We found significant different rates of learning burnout between different grades students (χ2 = 14.723, P<0.01). Additionally, we also found different rates of learning burnout’s dimensions (P <0.05). However, we did not found differences in the prevalence of learning burnout, dejection, and reduced personal accomplishment between genders and age groups, but the results indicated that the proportion of improper behavior increased with age (χ2 = 11.209, P <0.05).
Table 2. Differences in learning burnout and its subscales by demographic and online learning characteristics.
We found no association between students’ residences and learning burnout (P=0.107) and dejection (P=0.222). But students whose family located in economically developed cities such as municipalities/provincial capitals had less improper behavior and lower personal accomplishment compare to the less developed areas (χ2 = 9.624, P <0.05; χ2 = 14.336, P <0.01). In contrast, an analysis of household income indicated significant relationships with learning burnout. We also found 55.33% and 54.82% students with household income of less than 7250 $ reported higher improper behavior (χ2 = 11.021, P <0.05) and lower personal accomplishment (χ2 = 16.233, P <0.05). Besides, our survey results showed whether to be a class leader or obtain a scholarship during college had no relationships with learning burnout. However, students, who gain a scholarship during college, presented with less symptoms of improper behavior and personal accomplishment than those who have not. With respect to online learning characteristics, the results showed that the total score of learning burnout was lower with the increase of learning time, communication times and learning satisfaction.
- Analysis of the association between SSRS and its dimensions according to demographic and online learning characteristics
We selected variables that had an impact on learning burnout to explore its influence on each dimension of SSRS (Table 3). We found that grade was not associated with SSRS and its dimensions (P=0.677, P=0.343, P=0.808, P=0.358). We also found that students with high family income were more likely to receive subjective support and social support than the population with low family income. In addition, the results showed that with the increase of learning time, communication times and learning satisfaction, the scores of subjective support, utilization of support and SSRS were all higher (P <0.05), although most correlation coefficients were small.
Tables 3. Correlations of social support and learning characteristics (Spearman rho).
- Analysis of the relationship between SSRS and learning burnout
We used student’s t tests and binary regression to identify if students’ social support was affected by various learning burnout symptoms. Results showed that regardless of which subscale of learning burnout, SSRS scores were all decreased if they exhibited the corresponding symptoms. Besides, we found an inverse relationship between social support and learning burnout (odds ratio, 0.929 for 1-point increase in social support score; 95% CI, 0.898 - 0.961; P< 0.01) (Table 4). Students, who showed a syndrome of learning burnout, had a lower score on subjective support and utilization of support (t= 4.510, P <0.01; t= 4.158, P <0.01), but no difference in the objective support (t= 1.128, P >0.05). (Table 5)
Table 4. Differences in students’ SSRS scores by learning burnout, dejection, improper behavior and reduced personal accomplishment groups
Table 5 Differences in the subscale of social support scores according to the presence of learning burnout syndrome