Liu Yifan, Lyu Donghao, Xie Sujie, Yao Yuntao, Liu Jun, Lu Bingnan, Zhang Wei, Xian Shuyuan, Yan Jiale, Gong Meiqiong, Wu Xinru, Li Yuanan, Zhang Haoyu, Zhou Jiajie, Zhou Yibin, Lin Min, Yin Huabin, Wang Xiaonan, Wang Yue, Chen Wenfang, Zhang Chongyou, Du Erbin, Lin Qing, Huang Zongqiang, Xu Dayuan, Zhang Jie, Huang Runzhi, Ji Shizhao, Pan Xiuwu
Department of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
BGI research, BGI-Hangzhou, Hangzhou, 310012, China.
BMC Med Educ. 2025 Mar 27;25(1):448. doi: 10.1186/s12909-025-06977-3.
Medical school learning environment (MSLE) is highly related to medical students' academic performance. However, the grade point average (GPA) rankings have not been studied together with MSLE. We aim to figure out the relationship between GPA rankings and MSLE.
We gathered data from 12 medical schools in mainland China, employing the Johns Hopkins Learning Environment Scale (JHLES) to assess students' subjective perceptions of MSLE. Subsequently, we performed a cross-sectional study examining GPA rankings and JHLES scores. We conducted Pearson's Chi-square test and Welch's analysis of variance (ANOVA) with GPA rankings as the exposure variable and JHLES score as the outcome variable. Furthermore, we conducted a multivariate logistic regression analysis. Additionally, we developed a nomogram to forecast the outcome of JHLES and evaluated the model's accuracy and performance.
In Pearson's Chi-square test and Welch's ANOVA. We found a statistically significant difference (p < 0.001) between GPA rankings and JHLES scores. Specifically, students with higher GPA rankings might have a significantly higher proportion of high JHLES scores than those with lower GPA rankings. Through a multivariate logistic regression analysis involving seven variables, including GPA rankings, we took the group whose GPA ranked in the top 20-50% of the population as our reference benchmark. We obtained the odds ratio (OR) values for all GPA groups, along with their 95% confidence intervals (CI) and corresponding p-values. Notably, a nomogram containing seven variables was constructed. Diagnosed by decision curve analysis (DCA), a Receiver Operating Characteristic (ROC) curve, and a calibration curve plot, the nomogram was considered accordant (AUC = 0.627) and accurate.
GPA ranking is an independent predictor of MSLE. Students with higher GPA rankings are more likely to have higher JHLES scores, which in turn indicates higher satisfaction with the learning environment.
医学院校学习环境（MSLE）与医学生的学业成绩高度相关。然而，平均绩点（GPA）排名尚未与MSLE一起进行研究。我们旨在找出GPA排名与MSLE之间的关系。
我们收集了中国大陆12所医学院校的数据，采用约翰霍普金斯学习环境量表（JHLES）来评估学生对MSLE的主观感受。随后，我们进行了一项横断面研究，考察GPA排名和JHLES分数。我们以GPA排名为暴露变量、JHLES分数为结果变量进行了Pearson卡方检验和Welch方差分析（ANOVA）。此外，我们进行了多因素逻辑回归分析。另外，我们绘制了列线图以预测JHLES的结果，并评估模型的准确性和性能。
在Pearson卡方检验和Welch方差分析中。我们发现GPA排名与JHLES分数之间存在统计学显著差异（p < 0.001）。具体而言，GPA排名较高的学生获得高JHLES分数的比例可能显著高于GPA排名较低的学生。通过涉及包括GPA排名在内的七个变量的多因素逻辑回归分析，我们将GPA排名处于人群前20%-50%的组作为参考基准。我们获得了所有GPA组的优势比（OR）值及其95%置信区间（CI）和相应的p值。值得注意的是，构建了一个包含七个变量的列线图。通过决策曲线分析（DCA）、受试者工作特征（ROC）曲线和校准曲线绘制进行诊断，该列线图被认为具有一致性（AUC = 0.627）且准确。
GPA排名是MSLE的独立预测因素。GPA排名较高的学生更有可能获得较高的JHLES分数，这反过来表明对学习环境的满意度更高。
