The impact of grade point average on medical students’ perception of the learning environment: a multicenter cross-sectional study across 12 Chinese medical schools

Abstract

Background

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.

Methods

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.

Results

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.

Conclusion

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.

Introduction

The learning environment (LE) encompasses the physical spaces where learners study, their psychological and emotional well-being, the available resources and instructional materials, as well as the support they receive from peers, educators, and family members [1]. The learning environment is intimately linked to students' academic performance [2]. In light of this, LE serves as a vital metric for assessing higher education institutions. Medicine being a highly specialized discipline, is different from other disciplines. Medical students are primarily taught by medical doctors and are required to undergo practical training in hospitals [3]. Thus, a focused assessment of the medical school learning environment (MSLE) is required to enhance medical education. Studies from hospitals in America have indicated that the quality of MSLE greatly affected the quality of health services provided by graduated students [4]. Previous research has confirmed that financial restraints and demanding clinical work have strained the physical energy and mental health of the medical trainees and directors, leading to negative effects on the MSLE [5]. Although studies have explored the use of patient outcomes and grades to evaluate the MSLE [6, 7], there is a lack of analysis on the specific factors that independently influence students’ perceptions of the MSLE.

Grade point average (GPA) is a standardized numerical representation of a student's academic performance in a semester or an academic year [8]. It is calculated by assigning specific point values to the grades earned in courses and then integrating these points to determine an overall GPA score through a weighted average algorithm that multiplies grades for different courses by corresponding credit weights. Due to its consideration of both credits and grades, GPA reflects both the quality and quantity of students' learning, making it widely used across various institutions of higher education [9]. As a key indicator of students’ academic achievement, GPA has the potential to be a factor related to MSLE. However, existing studies have not deeply analyzed how the GPA, as an exposure factor, influences students' evaluations of the MSLE.

In our research, we collected 10, 901 questionnaires from students at China medical universities, including their GPA ranking, JHLES scores, and other MSLE-related information [10, 11]. We used students’ GPA rankings within their respective colleges, expressed as a percentage, as the standard measure. The Johns Hopkins Learning Environment Scale (JHLES) was one of the most comprehensive and widely used scales for assessing the MSLE. Its application in evaluating the learning environment of medical schools was proven effective [12, 13]. By applying Pearson’s Chi-square test, Welch's ANOVA, and multivariate logistic regression to this data, we intended to explore and elucidate the relationship between GPA ranking and MSLE using a retrospective approach. Additionally, we generated a nomogram for predicting MSLE after considering all statistically analyzed factors in the multivariate logistic regression. It would be an honor that our research could offer valuable guidance to medical instructors and clinical investigators in associated disciplines.

Materials and methods

Data source

This study was approved by the Ethics Committee of the First Affiliated Hospital of Naval Military Medical School (CHEC2023-284).

From 20th February to 31st March 2020, we initiated a cross-sectional study spanning 12 mainland China universities. These institutions were categorized into six groups. 985 Project Universities: Peking University and Tongji University. 211 Project University: Zhengzhou University. Military Medical Schools: The Air Force Medical University and Naval Medical University. First Batch of Medical Universities: Capital Medical University, Harbin Medical University, Fujian Medical University, Chongqing Medical University, and Southwest Medical University. Second Batch of Medical Universities: Mudanjiang Medical College. Non-985/211 Project University: Jinggangshan University. The 985 Project, launched in 1998, aimed to elevate fewer than 40 top Chinese universities to world-class status by providing substantial government funding. Similarly, the 211 Project was designed to strengthen around 100 universities by focusing on improving key disciplines in the twenty-first century. In contrast, the military medical schools and medical universities specialized exclusively in medical education and research. Medical students from each grade at the aforementioned 12 universities were invited to complete a questionnaire based on JHLES, which captured their perceptions of MSLE.

Initially, we conducted a pilot study at the Naval Medical University, selecting 20 students through stratified random sampling based on their grades. We assigned 15 students from grades 1 to 5, and 5 graduated students with a code based on their student number. We then incorporated them into a random number table for their respective grade. Additionally, we simplified the specific terms for easier comprehension in Chinese; for instance, “student” was changed to “classmate,” “school” to “medical school,” and “clinical” to “clinical field.” We either removed or replaced any potentially biased terms with neutral language. The students' feedback was instrumental in refining the questionnaire. As a result, the accuracy and fluency of the questionnaire were significantly enhanced [10, 11].

Following this, we digitized the questionnaire using Wenjuanxing (https://www.wjx.cn/), a web-based survey tool, and dispatched the survey link to the designated leaders of the aforementioned 12 medical schools. Subsequently, we implemented a stratified cluster random sampling method, categorized by grade. From each grade, 1 to 2 classes were picked at random through a lottery system, and all students in the chosen classes were invited to finish the questionnaire. All surveys were distributed in a consistent format. Once the questionnaires were returned, those with inconsistent responses or missing data were deemed invalid and excluded from the final record. We confirmed that our study adhered fully to the principles outlined in the Declaration of Helsinki.

Instrument

In this study, the primary focus was on examining the influence of GPA on MSLE evaluations. The JHLES (Table S1) was employed as the tool for assessing MSLE. The scale comprises seven subscales: Community of peers, Faculty relationships, Academic climate, Meaningful engagement, Mentoring, Inclusion and safety, and Physical space, totaling 28 items. Each item was assessed using a five-point Likert response scale ranging from strongly disagree (1) to strongly agree (5) (Table S2). All questions were revised into positive declarative sentences to better align with the Likert scale format. The final score of 28 items altogether was calculated. Higher scores indicated a more positive perception of the learning environment. Previous research demonstrated the reliability and validity of the JHLES scale [12, 14].

This study divided the participants' GPAs into five categories based on their rankings within the same major from the previous academic year: top 5%, 5–20%, 20–50%, 50–80%, and 80–100%. In China, universities used various GPA scales, including the 4.0, 4.3, and 5.0 systems. This diversity in grading systems posed a challenge for multi-center studies, as it required a standardized way to quantify students’ GPAs objectively. To address this, respondents indicated their ranks within the respective GPA intervals in the questionnaire instead of providing specific GPA scores.

Statistical analysis

Descriptive statistics were used to summarize categorical variables and continuous variables. Categorical variables were expressed as counts (percentages). Continuous variables were expressed as either mean (standard deviation, SD) or median (interquartile range). Age, gender, university category, university, major, ethnicity, only child, grade, native place, educational system, GPA ranking, father's education level, father's occupation, mother's education level, mother's occupation, learning environment of your schools, doctor-patient relationship in your hospitals, interests of medicine, and JHLES category were collected as categories variables in our analyses. Only JHLES (score) was a continuous variable. To classify JHLES scores, participants were divided into low and high groups based on the median value (104 points). Pearson’s Chi-squared test was used to assess the association between GPA rankings and JHLES categories (low and high). Welch’s ANOVA tests whether there was a significant mean difference in JHLES scores across GPA rankings. The association between GPA ranking and JHLES was investigated through the application of Pearson’s Chi-square test and Welch’s ANOVA. Incorporating GPA ranking and other variables, a multivariate logistic regression model was constructed to establish its independent influence on JHLES. Subsequently, a predictive nomogram was developed to estimate JHLES scores. Discrimination and calibration performances of the nomogram were assessed using receiver operating characteristic (ROC) curves and calibration curves, respectively. Decision curve analysis (DCA) was employed to assess the practical benefit for medical students. All statistical analyses were performed using R version 4.2.2 (Institute for Statistics and Mathematics, Vienna, Austria) and SPSS 20.0 (SPSS Inc., Chicago, IL, USA). A significance level of p < 0.05 (two-sided) was considered for statistical significance.

Results

Sample characteristics

To fully understand the inclusion and exclusion criteria and the whole progress of our work, a flowchart is shown (Fig. 1). Out of 11, 265 questionnaires received, 10, 901 were eligible for further analysis. The majority of students fell within the 16–25 age range (98.09%), and the majority gender was female (59.91%). Clinical medicine was the most prevalent major (79.52%), and the distribution of students across grades was highest in grade 1 (34.86%), followed by grade 2 (18.74%). A 5-year educational system was followed by over two-thirds of students (69.91%). GPA distribution indicated that most students had a GPA ranking of 20–50% in their major (35.26%), with the second-largest group having a GPA ranking of 50–80% (24.65%). Regarding parental education, most students' parents had a lower education level. In terms of the LE of the school, a significant proportion of students had positive perceptions, with 55.48% considering it good and 21.84% rating it excellent. The preferred learning styles among medical students were accommodation (33.90%) and assimilation (29.47%). Furthermore, a considerable number of students expressed interest in medicine (56.37%) (Figure S1,Table 1).

Fig. 1

Inclusion and Exclusion Criteria and Process Flowchart. The upper part is the inclusion and exclusion criteria, and the lower part is the flow chart of our work

Table 1 Characteristics of 10,901 patients

Pearson’s Chi-square test and Welch’s ANOVA

Pearson’s Chi-square test and Welch’s ANOVA were employed to further investigate the relationship between GPA and JHLES scores. As shown in Fig. 2A, significant differences in JHLES scores were observed among participants in different GPA ranking groups (p < 0.001). Specifically, individuals with a GPA ranking falling within the top 80–100% of their peers are less likely to exhibit high JHLES scores than those with a higher GPA ranking (39%). Conversely, participants with GPAs ranked in the top 5% group had significantly higher JHLES scores (69%) compared to those with lower GPA ranking (p < 0.001). Notably, a potential linear relationship was observed between GPA and JHLES, indicating that lower GPA was associated with a greater likelihood of lower JHLES scores, and vice versa. Figure 2B displayed the results of Welch’s ANOVA, showing that as GPA rankings increased in different groups, the JHLES scores tended to increase. The outcomes were significant (p < 0.001) and the potential linear relationship was shown.

Fig. 2

A Pearson’s Chi-square test for GPA ranking and JHLES. The percentages of high and low JHLES categories are shown. B Welch’s ANOVA for GPA rankings and JHLES. The violin graft showed the JHLES mean value and the distribution of each GPA group

We further explored whether the above conclusions held true across different age and gender subgroups. In Fig. 3A, participants from different age groups showed similar significant associations between GPA rankings and JHLES scores (p < 0.001). Among participants aged 16–20, 21–25, and 26–40, those with GPA ranking in the bottom 20% had a respective probability of 61%, 62%, and 43% to obtain low JHLES scores (p < 0.001). However, those with GPA ranking in the top 5% had a probability of only 34%, 29%, and 25%, respectively, to obtain low JHLES scores (p < 0.001). Figure 3B presented the results of Welch’s ANOVA analyzing the relationship between GPA rankings and JHLES scores conditioned on students aged 16–20, 21–25, and 26–40. It was evident that in the 16–25 age group, higher GPA rankings corresponded to higher JHLES scores (p < 0.001). However, among the 26–40 age group, the results obtained were not worth further investigation due to their small sample size (p = 0.85).

Fig. 3

A Pearson’s Chi-squared test results of GPA rankings and JHES for different age subgroups. The percentages of high and low JHLES categories are shown. B Welch’s ANOVA results of GPA rankings and JHLES for different age subgroups. The violin graft showed the JHLES mean value and the distribution of each GPA group

Across different gender groups, male and female participants exhibited consistent conclusions with the overall findings, with higher GPA ranked students tending to have higher JHLES scores (Fig. 4A,B). Based on the above statistical analyses, GPA ranking significantly influenced JHLES, with higher GPA ranking groups generally obtaining higher JHLES scores, whereas lower GPA ranking was indicative of poor JHLES outcomes.

Fig. 4

A Pearson’s Chi-squared test results of GPA rankings and JHLES for different gender subgroups. The percentages of high and low JHLES categories are shown. B Welch’s ANOVA test results of GPA rankings and JHLES for different gender subgroups. The violin graft showed the JHLES mean value and the distribution of each GPA group

Multivariate logistic regression analysis

Based on the results of univariable analysis, clinical experience and observation, and rigorous attempts, seven variables selected (age, gender, ethnicity, major, grade, native place, and GPA) were added to the multivariable logistic regression model. By conducting a multiple-factor logistic regression on these 7 variables, we confirmed that the impact of GPA on JHLES scores was significant (p < 0.001). Compared to the baseline group of GPA ranked in the top 20–50%, the odds ratio for the group with GPA ranked in the top 5% was 0.52 (95% CI: 0.44–0.62). It indicated that students in the top 20–50% of GPA were less likely to give high JHLES scores than students in the top 5% of GPA. That was, students in the top 5% of GPA were more likely to receive high JHLES scores than students in the top 20–50% of GPA. The odds ratio for the group with GPA ranked in the top 5–20% was 0.76 (95% CI: 0.69–0.85), and for the group with GPA ranked in the top 50–80%, the odds ratio was 1.48 (95% CI: 1.34–1.64). Lastly, for the group with GPA ranked in the top 80–100%, the odds ratio was 1.87 (95% CI: 1.62–2.12). This suggested that students in the top 20–50% of GPA were more likely to have high JHLES scores than those in the top 80–100% of GPA. It could be observed that a higher GPA ranking within the population was associated with a greater likelihood of obtaining higher JHLES scores. Complete and detailed variable details could be found in Table 2.

Table 2 Multivariate logistic regression analysis of JHLES scores

Nomogram construction and validation

A nomogram was drawn based on the results of the multivariate logistic regression. It was constructed for visualization and predicting JHLES scores (Fig. 5A). Elevated scores on the nomogram corresponded to an increased probability of diminished JHLES scores. Regarding GPA ranking in the nomogram, the results were consistent with previous findings. The group with the best GPA ranking (top 5%) was assigned the lowest score (0), while the group with the worst GPA ranking (80–100%) was assigned the highest score (100). Additionally, GPA ranking showed a monotonic increasing relationship with the assigned scores in the nomogram. Complete and detailed variable details could be found in Tables 3 and 4.

Fig. 5

A Nomogram for predicting low JHLES probability. The total points present the chance to attain a low JHLES score. B Decision curve analysis curve of the nomogram. The prediction accuracy of this model is confirmed by the blue curve. C Receiver Operating Characteristic train of the nomogram. The area under the curve (AUC) for the ROC train curve was 0.633, for the ROC test curve was 0.612, and for the ROC total was 0.627. D Calibration curve of the nomogram. The model’s predicted probability fit in the actual rate

Table 3 Scores of each variable in the nomograms

Table 4 Probability of low JHLES

Subsequently, we assessed the accuracy of the nomogram. As shown in Fig. 5B, in the DCA curve, the non-adherence prediction nomogram was above the baseline, indicating that this model was valuable for predicting JHLES outcomes. In Fig. 5C, the area under the curve (AUC) for the ROC train curve was 0.633, for the ROC test curve was 0.612, and for the ROC total was 0.627, indicating good accuracy of the nomogram. Finally, in the calibration curve plot (Fig. 5D), the nomogram closely approximated the diagonal line, suggesting that the model’s predicted probabilities were in close agreement with the observed frequencies.

Discussion

In medical education, MSLE is of paramount importance. A favorable MSLE can support the physical and psychological well-being of medical students. MSLE not only changes the students enrolled in school now [15], but also has a lasting impact on their careers after graduation [16]. Medical disciplines involve significant difficulty and demand high standards, which correspondingly elevate the expectations for MSLE. Additionally, the learning process in clinical work differs from other disciplines' instructional methods, necessitating a distinct approach to evaluating the LE.

Our study focused on the correlation between GPA ranking and JHLES, aiming to investigate whether GPA ranking influenced the participants' perceptions of MSLE. By using GPA ranking, we minimized the impact of differing GPA scales across institutions, allowing us to integrate data from 12 Chinese medical university. Through systematic analysis, we found that higher GPA rankings were more likely to be associated with higher JHLES scores. This conclusion indicated that students with better academic performance in medical schools tended to have more positive evaluations of the MSLE.

To elucidate how GPA ranking can influence JHLES scores and students' feelings about MSLE, both subjective and objective factors are enumerated here. We category the factors into 3 categories. Students’ emotional stability, learning attitude and motivation, sleep quality, and mental health simultaneously influence both GPA ranking and students’ perceptions of MSLE. Students’ academic pressure, peer interactions, and self-efficacy are shaped by GPA ranking and in turn affect how students perceive the MSLE. Additionally, quality of teaching and mentorship, learning resources and facilities, and curriculum structure and organization are directly tied to students’ perceptions of MSLE, which subsequently impact their GPA ranking.

Factors affecting both students’ GPA ranking and perception of MSLE

Research has shown a significant correlation between emotional stability and GPA performance, indicating that students with stronger emotional regulation skills tend to perform better in their exam scores [17]. The ability to effectively control one's emotions translates to better utilization of study time, thus providing advantages in exams or essay writing. Meanwhile, stable emotions lead participants to perceive themselves as being in a conducive environment. On the contrary, emotional dysregulation and internalizing disorders lead to a higher possibility for adolescents to report worse feelings of the environment [18]. In short, emotional regulation is an essential ability for medical students, influencing both their GPA ranking and their perception of the MSLE.

The students' learning attitudes and motivation potentially influence their academic performance and GPA rankings, while also affecting their perception of the MSLE. Positive learning attitudes, strong motivation, and goal-oriented behavior may drive medical students to become more focused and diligent in their studies, ultimately leading to improved academic achievements and higher GPA rankings [19]. Simultaneously, having a determined motivation has a positive effect on students’ feelings on MSLE. Studies have shown that, mediated by individuals’ learning attitudes and motivation, a positive clinical practice environment is associated with greater innovative behavior [20, 21]. Positive attitudes and strong motivation enhance students’ sense of identification with the learning environment, making them more adaptable and receptive.

Mental health potentially impacts both students' GPA and their JHLES scores. The complex learning materials and integration of theoretical knowledge and practical skills in medical education lead to elevated academic pressure among medical students, increasing the likelihood of experiencing anxiety and depression. Anxiety and depression could potentially disrupt students' learning and performance, resulting in lower GPA rankings [22]. Research indicated that students with depression tended to have significantly lower GPA rankings [23, 24]. Moreover, students with suboptimal mental health were more likely to perceive their environment negatively. These issues might negatively impact students' level of engagement in learning, teacher-student relationships, and the overall learning atmosphere.

Sleeping takes up about 1/3 of the time in everyone’s daily life [25]. Insufficient sleep reduces students’ focus, short-term memory, comprehension, and judgment, all of which are closely tied to their academic performance [26, 27]. Meanwhile, poorer exam outcomes also have negative impacts on students’ sleep quality, which leads to a vicious circle [28]. Students who suffer from terrible sleep quality are facing great physical and mental pressure. However, in Chinese culture, it is a shame to admit one’s pressure and fragility [29]. Students tend to blame the environment rather than seeking help. So, sleeping quality both changes students' perception of MSLE and affects their GPA.

Factors affected by students’ GPA rankings could influence their perception of MSLE

GPA rankings would deeply impact the academic pressure of Chinese medical students. In Chinese medical universities, students’ futures are directly affected by their GPA rankings. Most institutions allocate guaranteed graduate program placements based solely on GPA ranking. Furthermore, leading medical universities prioritize GPA ranking as a key indicator of student competency during admissions. This emphasis places considerable academic pressure on students to maintain a high GPA ranking. Research has proved that academic pressure is correlated to study environment in medical students [30]. High academic pressure would negatively impact students’ mental health, alter their learning attitudes, and reduce effective use of available resources. These effects can lead students to evaluate their learning environment more negatively.

Students' GPA rankings significantly influence their peer interaction. Higher GPA rankings encourage traits in students including sociability, cooperativeness, and receptivity to new experiences [31]. Higher GPA ranking students possibly perceive more support from faculty and more opportunity of scholarship, honors, and awards. These can explain why high GPA ranking students tend to believe their MSLEs are positive.

GPA ranking are related to self-efficacy of students [8]. Higher GPA rankings reinforce students’ belief in their ability to succeed academically, which in turn enhance their self-efficacy [32]. This sense of accomplishment fosters confidence and positive self-efficacy, motivating them to tackle challenging tasks with greater resilience and persistence. Superior self-efficacy can positively shape students’ perceptions of their MSLE. When students believe in their capabilities and accurately assess their own effectiveness, they tend to view challenges within the learning environment as opportunities for growth rather than obstacles. Students with high self-efficacy fosters a more adaptive, open, and engaged attitude toward their surroundings, further enhancing their experience and sense of belonging within the academic setting.

Factors that belong to the learning environment would influence students’ GPA ranking

The quality of teaching and mentorship is often discussed in learning environment [33, 34]. Students commonly view high-quality faculty as an essential component of an excellent learning environment. Positive perceptions of teaching quality can enhance students’ understanding of material, leading to better academic performance and higher GPA rankings.

Learning resources and facilities are objective factors when it comes to MSLE. Overcrowded classrooms can increase students' psychological distress and negatively impact their academic performance, subsequently influencing their subjective evaluation of MSLE [35]. Additionally, within unsatisfied environment, students who are more sensitive to their surroundings may be more significantly affected, leading to poorer GPA performance. A university equipped with advanced instruments can enhance the students' experience with the course. Students who recognize the advantages of advanced facilities tend to have a positive perception of the MSLE. This awareness encourages them to value learning opportunities, which can, in turn, support higher GPA performance.

The curriculum structure and organization are established as an important part of MSLE. An effectively structured faculty team enhances student satisfaction with the MSLE [34]. A well-designed curriculum promotes deeper student engagement and understanding [36]. Students who fit in the curriculum structure tend to be optimized with the MSLE, while having superior GPA rankings.

To summarize the factors that influenced the GPA and MSLE, a mechanism diagram was created (Fig. 6).

Fig. 6

Mechanism diagram in discussion section. We illustrated the factors associated with higher GPA rankings that contributed to students’ positive perceptions of the MSLE. Then, we identified the factors resulting from an ideal MSLE, which offered additional benefits to students’ GPA rankings. Lastly, we listed the factors that influenced both students’ GPA rankings and their perceptions of the MSLE

Implications and suggestions

Chinese medical schools should take a multifaceted approach to enhance the learning environment and support students’ GPA and overall development. The following are specific suggestions derived from this research:

Prioritize emotional stability and mental health support

Medical schools should increase investment in mental health services, providing regular mental health screenings on emotional regulation. Incorporating mindfulness and stress management into the curriculum can also equip students to navigate high-pressure environments more effectively.

Promote sleep and time management awareness

Schools can offer sleep health education. Students experiencing sleep impairment could benefit from skills and knowledge in time management, strategies for reducing late-night study sessions, and techniques to enhance productivity. By developing these competencies, they may improve sleep quality, academic performance, and perception of MSLE.

Reduce academic pressure by diversifying evaluation metrics

Schools should broaden evaluation metrics to include clinical performance, research contributions, and practical experience. Diversified assessments can help reduce GPA-related pressure and encourage students to develop skills beyond academic achievement.

Enhance teaching quality and faculty development

Schools should invest in faculty training, particularly in communication, empathy, and innovative teaching methods, to foster effective faculty-student interaction [11, 37]. Regular feedback from students on teaching quality, followed by responsive improvements, can further elevate satisfaction and learning experiences.

Establish flexible and adaptive curriculum structures

Schools should regularly review and update curriculum content, incorporating elective courses and flexible scheduling to allow students to explore areas of interest and manage workload effectively.

By implementing these strategies, medical schools can improve academic performance of students and promote a positive perception of the learning environment. These adjustments support the holistic development of future medical professionals, providing a strong foundation for long-term success.

Limitations

This cross-sectional study has several limitations. As it is cross-sectional in design, it only indicates correlation rather than causation. This restricts our ability to ascertain whether variations in MSLE perceptions are a result of GPA rankings or other underlying factors. Secondly, despite the clear five-point Likert scale provided in the questionnaire, individual subjective responses may still affect the accuracy and reliability of the data due to possible reporting bias. Additionally, while a stratified random sampling technique was employed to select participants, systematic errors may still have influenced the sample. Cross-validation with new data could help determine if the associations observed are replicable. Finally, future research should explore factors and potential interventions for students with less favorable views of the MSLE, providing essential insights for medical educators and policymakers. However, this study marks the first of its kind in China to explore the intrinsic link between GPA rankings and MSLE perception using a large, multi-center sample that enhances its representativeness. Studies with stronger methodologies, such as case–control or cohort designs, are recommended to further validate the link between perceptions of MSLE and other factors.

Conclusion

High GPA rankings significantly optimized students’ perception of MSLE. GPA ranking was an independent predictive factor for perception of MSLE.