A systematic review was conducted on the literature on feeding behaviors in Chinese families of children under 6 years old. Forty relevant publications were identified, of which 33 were published in Chinese, 7 in English. All studies were questionnaire-based and used a cross-sectional research design. Approximately half of the studies reported a score for each feeding practice/style, based on a Likert scale; the other half dichotomized these scores into a percentage of the population that reported frequent use of the behaviors. The most commonly reported feeding style of Chinese caregivers was a locally defined "active response" style that somewhat resembled authoritative parenting. The most commonly reported feeding practices were praise, encouraging trying new foods, encouragement of balanced diet and encouragement of healthy eating. Some behaviors showed a great deal of variance in prevalence between studies, which may be at least partially due to differences in methodology and how behaviors were defined. Some feeding behaviors varied in frequency depending on the child's age, although longitudinal studies are needed to better understand how these evolve over time. Child body composition was also associated with feeding behaviors use, although the direction of the association cannot be determined due to the cross-sectional nature of the research. There is still an important gap in the literature regarding the feeding behaviors of non-maternal caregivers, as grandparents often play an important role in childcare in China.

Background: Studies exploring influencing factors of emotional engagement among medical students are scarce. Thus, we aimed to identify influencing factors of medical students’ emotional engagement. Methods: We carried out a multi-center cross-sectional study among 10,901 medical students from 11 universities in China. The Chinese version of Utrecht Work Engagement Scale-Student version (UWES-S) was used to evaluate emotional engagement level of medical students. The predictors related to engagement level were determined by the logistic regression analysis. Furthermore, we constructed a nomogram to predict emotional engagement level of medical students. Results: A total of 10,576 sample were included in this study. The mean emotional engagement score was 74.61(± 16.21). In the multivariate logistic regression model, we found that males showed higher engagement level compared with females [odds ratio (OR) (95% confidence interval (CI)): 1.263 (1.147, 1.392), P < 0.001]. Medical students from the second batches of medical universities had higher engagement level and from “Project 985” universities had lower engagement level compared with 211 project universities [OR (95%CI): 1.376 (1.093, 1.733), P = 0.007; OR (95%CI): 0.682 (0.535, 0.868), P = 0.002]. Medical students in grade 4 and grade 2 presented lower engagement level compared with in grade 1 [OR (95%CI): 0.860 (0.752, 0.983), P = 0.027; OR (95%CI): 0.861 (0.757, 0.980), P = 0.023]. Medical students lived in provincial capital cities had higher engagement level compared with in country [OR (95%CI): 1.176 (1.022, 1.354), P = 0.024]. Compared with eight-year emotional duration, medical students in other emotional duration (three-year and four-year) had lower engagement level [OR (95%CI): 0.762 (0.628, 0.924), P = 0.006]. Medical students’ engagement level increased with increases of grade point average and interest in studying medicine. Medical students learned by converging style showed lower engagement level [OR (95%CI): 0.827 (0.722, 0.946), P = 0.006] compared with accommodating style. The model showed good discriminative ability (area under curve = 0.778), calibrating ability and clinical utility. Conclusions: We identified influencing factors of medical students’ emotional engagement and developed a nomogram to predict medical students’ emotional engagement level, providing reference and convenience for educators to assess and improve emotional engagement level of medical students. It is crucial for educators to pay more attention to emotional engagement of medical students and adopt effective strategies to improve their engagement level.

Medical students' perceptions of the medical school learning environment (MSLE) have an important impact on their professional development, and physical and mental health. Few studies reported potential factors that influenced medical students' perceptions of MSLE. Thus, the main goal of this study was to identify influencing factors for medical students' perception levels of MSLE. The perception levels of MSLE were assessed by the Johns Hopkins Learning Environment Scale. The univariate and multivariate logistic regression analyses were performed to identify significant predictors for the perceptions of MSLE. The nomograms were established to predict medical students' perception levels of MSLE. In the multivariate logistic regression model, gender, university category, grade, mother education level, learning environment of schools, interests in medicine, and Kolb learning experience were significantly associated with medical students' perceptions of MSLE. Correspondently, the nomograms were built based on significant variables identified by the univariate logistic regression analysis. The validation of the nomograms showed that the model had promising predictive accuracy, discrimination, and accordance (area under the curve (AUC) = 0.751). This study identified influencing factors of medical students' perceptions of MSLE. It is essential to implement corresponding interventions to improve medical students' perceptions.

Background: Study engagement is regarded important to medical students’ physical and mental wellbeing. However, the relationship between learning environment of medical schools and the study engagement of medical students was still unclear. This study was aimed to ascertain the positive effect of learning environment in study engagement.Methods: We collected 10,901 valid questionnaires from 12 medical universities in China, and UWES-S was utilized to assess the study engagement levels. Then Pearson Chi-Square test and Welch’s ANOVA test were conducted to find the relationship between study engagement and learning environment, and subgroup analysis was used to eradicate possible influence of confounding factors. After that, a multivariate analysis was performed to prove learning environment was an independent factor, and we constructed a nomogram as a predictive model.Results: With Pearson Chi-Square test (p < 0.001) and Welch’s ANOVA test (p < 0.001), it proved that a good learning environment contributed to a higher mean of UWES scores. Subgroup analysis also showed statistical significance (p < 0.001). In the multivariate analysis, we could find that, taking “Good” as reference, “Excellent” (OR = 0.329, 95%CI = 0.295–0.366, p < 0.001) learning environment was conducive to one’s study engagement, while “Common” (OR = 2.206, 95%CI = 1.989–2.446, p < 0.001), “Bad” (OR = 2.349, 95%CI = 1.597–3.454, p < 0.001), and “Terrible” (OR = 1.696, 95%CI = 1.015–2.834, p = 0.044) learning environment only resulted into relatively bad study engagement. Depending on the result, a nomogram was drawn, which had predictive discrimination and accuracy (AUC = 0.680).Conclusion: We concluded that learning environment of school was an independent factor of medical student’s study engagement. A higher level of learning environment of medical school came with a higher level of medical students’ study engagement. The nomogram could serve as a predictive reference for the educators and researchers.

Empathy of medical students is crucial, yet it tends to decline as students enter later academic years. Empathy appeared to be affected by the learning environment (LE), which could be a potential contributor. We conducted a cross-sectional study to investigate the association between LE and empathy. The study comprised overall 10,901 medical students from 12 Chinese medical schools. Jefferson Scale of Empathy (JSE) was utilized to assess empathy, while a 5-point question and the Johns Hopkins Learning Environment Scale were employed to evaluate LE. JSE scores were compared across LE levels using Pearson Chi-square, Welch’s ANOVA, and univariable linear regression. After adjusting for covariates, multivariable linear and logistic regression were used to assess the independent connection between LE and empathy. Confounders were controlled by stratified subgroup analysis. LE was significantly associated with medical students’ empathy, as shown by the Pearson Chi-square test (p < 0.001) and Welch’s ANOVA (p < 0.001). In multivariable logistic regression analysis, lower JSE scores were linked to common, bad, and terrible LE (OR = 2.226, 95% CI = [2.011, 2.465], p < 0.001; OR = 2.558, 95% CI = [1.745, 3.751], p < 0.001; OR = 1.889, 95% CI = [1.131, 3.153], p = 0.015), while excellent LE was associated with higher JSE scores (OR = 0.348, 95% CI = [0.312, 0.388], p < 0.001). Linear regression and stratified subgroup analysis confirmed logistic regression’s findings. LE was a significant predictor for empathy. Better LE was associated with higher empathy and worse LE was related to lower empathy. This inspired us to improve LE to enhance medical students’ empathy.

Background: Medical school learning environment (MSLE) has a holistic impact on students’ psychosomatic health, academic achievements, and personal development. Students in different grades perceive MSLE in different ways. Thus, it is essential to investigate the specific role of student’s grade in the perception of MSLE. Methods: Using the Johns Hopkins Learning Environment Scale (JHLES) as a quantification instrument for the perception level of MSLE, 10,901 medical students in 12 universities in China were categorized into low or high JHLES group according to their questionnaires. We investigated the relationship between student’s grade and JHLES category by univariate analysis employing Pearson Chi-square test and Welch’s ANOVA. Then multivariable logistic regression analysis confirmed the predictive efficacy of student’s grade. A nomogram concerning the prediction of low JHLES score probability in medical students was also constructed. Results: A significant difference between two JHLES categories among students in different grades was observed (p < 0.001), with the proportion of the high JHLES group dominating in grade 1, 5, and the graduate subgroups (p < 0.001). The mean JHLES score declined especially in the third and fourth graders compared to freshmen (p < 0.001), while the mean score among the fifth graders had a remarkable rebound from the third graders (p < 0.001). Most imperatively, identified by multivariable logistic regression analysis, students in grade 3 (OR = 1.470, 95% CI = 1.265–1.709, p < 0.001) and 4 (OR = 1.578, 95% CI = 1.326–1.878, p < 0.001) perceived more negatively than freshmen. The constructed nomogram provided a promising prediction model for student’s low JHLES score probability, with accuracy, accordance, and discrimination (area under the curve (AUC) = 0.627). Conclusion: The student’s grade was a significant influencing factor in medical students’ perception of MSLE. The perceptions among the third and fourth graders got worse, probably due to the worrying changes in various aspects of MSLE during that period. The relevant and appropriate interventions to improve medical students’ perceptions are urgently needed.