Abstract
-
Purpose
This study employed latent class analysis (LCA) to classify medical students based on their pre-admission characteristics and examine differences in academic performance, mental health, and post-graduation career paths.
-
Methods
A total of 314 medical students who matriculated from 2015–2018 at a Korean medical school participated for this study. LCA was performed with their gender, region of origin, admission type, and gap years (i.e., a period for retaking the college entrance examination) as classification variables. Mental health was assessed using BDI-II (Beck Depression Inventory-II), SSI-Beck (Scale for Suicidal Ideation-Beck), and K-Scale (Korean Internet Addiction Scale). Academic outcomes and career paths were compared across latent classes through analysis of variance and regression analyses.
-
Results
Three distinct latent classes were identified in the total sample (n=314): the rolling admission–regional talent group (25.0% of the total sample), the regular admission–male retaker group (57.3%), and the non-local female group (17.7%). The regular admission-male retaker group showed significantly higher internet over- dependency levels (p<0.001), lower academic performance (p<0.001), and higher grade repetition rates (p<0.05) than the others. The rolling admission-regional talent group had the highest proportion of students working at their alma mater-affiliated hospitals (p<0.05).
-
Conclusion
The research findings could present practical implications to the medical school systems because this research analyzed the mental health status, academic performance, and career paths based on the admission types of medical school students. Furthermore, the results imply that a specific policy and/or a student support system should be required for medical students’ achievement and their successful transition to career.
-
Key Words: Academic performance, Latent class analysis, Medical students, School admission criteria
Introduction
The persistent shortage of physicians in non-metropolitan areas continues to pose a significant challenge in Korea. Numerous studies have consistently reported that the number of physicians is insufficient in rural or medically underserved areas compared to metropolitan areas, and this issue causes persistent medical imbalances [
1-
3]. This physician shortage not only undermines healthcare accessibility for residents in the areas but also brings serious problems from a public health perspective [
4].
To address this issue, medical schools in Korea have implemented various admission policies to resolve regional disparities and strengthen community-based medical education. One such policy is the regional talent admission track that prioritizes the selection of students from non-metropolitan and medically underserved areas [
5,
6].
Medical school admissions are primarily conducted through two pathways, which are rolling and regular admission. Rolling admission candidates are selected based on various factors including academic records, comprehensive school reports, while regular admission is evaluated primarily on the college entrance examination. Regional talent admission, which targets high school graduates from a specific region, serves as an important policy measure for securing regional medical personnel and constitutes a significant portion of medical school admissions. The Korean government recommends that medical schools allocate 60% of their admissions to regional talent admission track, and some institutions committing their full rolling admission quotas to this pathway.
As the admission process becomes more varied and complicated, particularly with the expansion of the regional talent admission track, the student population is becoming increasingly diverse in individual characteristics such as gender, region of origin, admission type, and gap years. This increasing heterogeneity may influence students’ academic performance, adjustment to medical school, and career paths.
Previous studies have reported that students admitted through the rolling admission tend to have higher academic performance and lower rates of grade repetition and dropout compared to those admitted via regular admission [
7-
9]. These results have also been confirmed in veterinary medicine, where students admitted through the rolling admission showed better academic performance than those admitted through the regular admission [
10]. In addition, some studies have shown that female students have higher academic performance than males [
8,
11,
12]. However, some studies have reported that gender or region of origin may not affect academic performance [
13,
14], and some studies have also shown that younger student at the time of admission could have better academic performance [
13].
International studies reinforce the notion that student background characteristics—such as gender, age, and academic preparedness—can influence success in medical education. In the United States, for instance, male students have demonstrated higher scores on certain standardized exams such as the MCAT (Medical College Admission Test) and USMLE (United States Medical Licensing Examination) [
15,
16], although overall academic performance does not consistently vary by gender [
17,
18]. In Japan, admission-related variables, including gender, age at entry, and region of origin, have been linked to graduation outcomes, licensing exam pass rates, and academic delays [
6,
19,
20]. Of particular note, it was found that despite having lower entrance scores, students admitted through the regional talent admission track achieved higher grade point average (GPA) during their studies, suggesting potential benefits of this admission pathway [
21].
The prevalence of depression among medical students is more than 4 times higher than that of the general Korean population [
22], and emotional problems experienced while adapting to a new environment can negatively affect academic performance and university life [
23]. In addition, medical students tend to experience higher levels of stress, interpersonal difficulties, and suicide risk compared to students in other fields [
24], highlighting the need for a systematic analysis of their mental health status.
However, most previous studies have employed a variable-centered approach, which examines individual factors such as gender or admission type in isolation, without fully accounting for the heterogeneity that might exist within the medical student population. Although the variable-centered approach could be useful to unveil relationships among specific variables, it may fall short in capturing the complex, multidimensional nature of individual students and the dynamic interactions.
To gain proper understanding of student diversity and to inform the development of targeted support strategies, a person-centered analytical approach is required. Latent class analysis (LCA) offers a robust, model-based method for identifying unobserved subgroups within heterogeneous populations based on shared patterns across categorical variables [
25]. In LCA, multiple models are compared to determine the optimal number of classes, and it accounts for measurement error, thereby enhancing the reliability and validity of subgroup identification [
26].
LCA is an analytical method that focuses on patterns formed by combinations of individuals’ multidimensional characteristics, enabling the identification of unobserved latent classes. While traditional approaches mainly explore the effects of individual variables such as gender or individual differences, LCA captures within-group heterogeneity by examining interaction patterns among multiple categorical variables. In particular, whereas variable-centered approaches may obscure structural differences or distinctive group characteristics due to their emphasis on average effects, LCA allows these differences to be identified more clearly.
Therefore, this study utilized LCA to classify medical students based on key pre-admission characteristics such as gender, region of origin, admission type, and gap years. This person-centered approach enables the identification of heterogeneous subgroups within student populations that share similar backgrounds. This study aims to identify latent classes and examine how they differ in terms of academic performance, academic retention rates, mental health status, and post-graduation career paths.
In sum, the current study aims to provide empirical evidence that can serve as a foundation for improving medical school admission policies and developing student-tailored educational support programs. Furthermore, it seeks to examine the effectiveness of the regional talent admission track in addressing imbalances in the distribution of medical personnel across regions.
Methods
1. Participants
This study analyzed 314 medical students who matriculated at a medical school in Korea from the 2015 to 2018 academic years. The sample consisted of 202 male students (64.3%) and 112 female students (35.7%). In terms of admission type, 150 students (47.8%) were admitted through rolling admission, of which 75 students (23.9%) were admitted through the regional talent admission track. With respect to geographic background, 180 students (57.3%) were from the same region as the location of the medical school. Regarding college entrance exam history, 122 students (38.9%) had re-taken the university entrance exam 1 or more times, while 192 students (61.1%) were admitted on their first attempt following high school graduation.
2. Instruments
This study analyzed cohort data collected from medical schools, focusing on admission type, academic performance, grade retention and leave of absences, and mental health status assessed during the pre-medical curriculum. Students were classified as local if their hometown matches the medical school location, and non-local if their hometown does not. A gap year is defined as the period between high school graduation and college entrance, and is mainly due to re-taking the university entrance exam.
Mental health data included the Beck Depression Inventory-II (BDI-II), the Scale for Suicidal Ideation-Beck (SSI-Beck), and the Korean Internet Addiction Scale (K-scale), all of which were assessed during the second semester of the first year of the pre-medical curriculum. The BDI-II, based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, is a 21-item self-report questionnaire rated on a 4-point Likert scale (0–3) which assesses depressive symptoms such as anxiety, low motivation, and difficulty concentrating [
27]. The SSI-Beck is a 21-item scale rated on a 3-point Likert scale (0–2), where only students who endorse suicidal thoughts on the initial five items proceed to complete the remaining questions; the total score is calculated from 19 items [
28]. The K-scale, developed by the Korea Information Society Agency, comprises 20 items rated on a 4-point scale (1–4) and measures core features of internet over-dependency, such as tolerance and immersion, enabling classification into addicted or at-risk groups [
29].
LCA was performed to identify subgroups based on latent characteristics, including gender, admission type, regional talent admission, and gap years.
Model fit was assessed with Information indices such as the Akaike information criterion (AIC), the Bayesian information criterion (BIC) and entropy values were utilized to assess model fit and to determine the number of latent classes. These indices allow comparison of model parsimony across different numbers of latent classes, with lower values indicating a better-fitting model [
30]. In addition, entropy values of 0.8 or higher were considered indicative of high classification accuracy [
31].
To examine differences among the identified classes, chi-square tests, one-way analysis of variance (ANOVA), and regression analyses were performed for variables such as final GPA, number of grade repetitions, and post-graduation career paths. The Bonferroni correction was applied for post hoc comparisons. All statistical analyses were conducted using Jamovi ver. 2.5.3 (Jamovi project).
4. Ethical considerations
This study was approved by the Institutional Review Board (IRB) of Keimyung University Dongsan Hospital (IRB approval no., DSMC 2024-02-024).
Results
1. Latent class classification
LCA was conducted to identify heterogeneous subgroups among medical school entrants based on admission type, region of origin, gender, and gap years. Model fit was evaluated using the AIC, BIC, and entropy for classification accuracy. The three-class model demonstrated the optimal model fit, with the lowest values for AIC (1,793.784), BIC (1,857.523) as well as an acceptable entropy value of 0.848. In contrast, models with four or more classes showed higher AIC and BIC values and lower entropy scores, indicating poorer classification performance. Based on these results, the three-class model was selected as the final solution due to its superior fit indices and adequate classification quality (
Table 1).
The relative proportions of each latent class were as follows: Class 1 accounted for 25.0% of the total sample, Class 2 for 57.3%, and Class 3 for 17.7%. The defining characteristics of each class are as follows.
Class 1, designated as the rolling admission-regional talent group, had an approximately equal gender distribution (50.6% male, 49.4% female), and all students were admitted through rolling admission (100.0%). The majority of students in this class entered via the regional talent admission track (95.5%), and all students were from the same region as the university (100.0%).
Class 2, designated as the regular admission-male retaker group, was characterized by a predominantly male composition (90.2%) and consisted mainly of students admitted through regular admission (75.8%). All students in this class were from regions different from that of the university (100.0%) and had the highest proportion of students with a gap year (71.6%).
Class 3, designated as the non-local female group, was composed entirely of female students (100.0%). The distribution between rolling and regular admissions was relatively balanced, and all students were admitted through non-regional talent admission tracks (100.0%). The majority (73.8%) were from regions different from that of the university, and approximately half (50.0%) had a gap year (
Fig. 1).
To examine differences in depression, suicidal ideation, internet over-dependency, and final GPAs among the three latent classes, one-way ANOVA was employed, and chi-square analysis was conducted to compare grade repetition, dismissal, and intern work location across the latent classes. The analysis results showed significant differences in internet over-dependency and final GPAs (p<0.001). Post hoc comparisons revealed that the regular admission-male retaker group showed significantly higher levels of internet over-dependency and lower final GPAs compared to the rolling admission-regional talent group and the non-local female group. Additionally, the regular admission-male retaker group had the highest rate of grade repetition (p<0.05). Regarding the proportion of interns working at the university’s affiliated hospital and hospitals located in the same region as the university, the rolling admission-regional talent group had the highest proportion, while the non-local female group had the lowest (p<0.05) (
Table 2).
To examine differences in internet over-dependency and final GPAs across latent classes, linear regression analysis was conducted using rolling admission-regional talent group as the reference. Regular admission-male retaker group showed significantly higher internet over-dependency (β=6.62, p<0.001) and lower final GPAs (β=–1.74, p<0.001) compared with rolling admission-regional talent group. Non-local female group did not differ significantly from rolling admission-regional talent group in either variable (
Table 3).
To examine differences in grade repetition and career pathways across latent classes, logistic regression analysis was conducted using rolling admission-regional talent group as the reference. Regular admission-male retaker group showed significantly higher odds of grade repetition (odds ratio [OR], 3.45; p<0.05), while non-local female group did not differ significantly. In terms of career paths, non-local female group was significantly more likely to work as an intern at their alma mater affiliated hospital (OR, 2.57; p<0.05), whereas the higher odds for regular admission-male retaker group were not statistically significant (
Table 4).
Discussion
This study classified medical students into distinct subgroups using LCA, based on background characteristics such as admission type, gender, region of origin, and gap years, and identified significant differences in academic performance, mental health status, and career paths.
First, the regular admission-male retaker group showed the lowest academic performance and the highest grade repetition rate. This group also exhibited high levels of internet over-dependency. These findings contrast with students admitted through the regional talent admission track, who generally adapted well to university life. Students with retaking experience may be more vulnerable to academic stress and psychological burden during their transition to medical school, and such stress may lead to internet dependence and decreased academic performance. These outcomes may be better understood as resulting from decreased academic motivation coupled with psychosocial vulnerabilities, rather than cognitive deficiencies. In contrast, the rolling admission-regional talent group had the lowest grade repetition rate [
7-
10,
20,
21], which is consistent with previous studies reporting that younger age is associated with higher academic achievement [
13].
Medical school requires sustained and systematic learning efforts due to its extensive academic workload and distinctive curricular structures, such as block- and quarter-based systems. In this context, students admitted through rolling admission appeared to adapt more smoothly to the medical school learning environment, potentially benefiting from their prior experiences with consistent academic self-management during high school. By contrast, students admitted through regular admission tended to encounter greater challenges during the adaptation process, which may be attributable to a partial misalignment between their predominantly test-oriented learning experiences and the self-directed and integrative learning approaches emphasized in medical education.
In particular, the regular admission–male retaker group exhibited relative vulnerability in both academic achievement and psychological adaptation. However, this finding should be interpreted not as a deficit in cognitive capacity, but rather as a dissonance between the learning strategies shaped in high-stakes, exam-centric environments and the pedagogical approaches of medical curricula. It is noteworthy that the experience of successfully navigating repeated college entrance exams reflects a high degree of academic persistence and goal commitment. This suggests that, given appropriate intervention, this group possesses powerful motivational assets to successfully complete the medical curriculum. Therefore, rather than labeling them as an inherently problematic group, it is necessary to establish tailored academic coaching and pedagogical support systems that facilitate the effective transfer of their inherent academic tenacity into the medical school learning framework.
Second, the non-local female group demonstrated the highest academic performance and the lowest level of internet over-dependency among the latent classes, indicating effective adaptation to the medical school curriculum. However, this group was least likely to choose career paths at alma mater-affiliated hospitals or hospitals located in the university region. These findings suggest that although non-local female students perform exceptionally well academically [
8,
11,
12], they are less likely to settle in the region as physicians. Therefore, to enhance regional retention among this group, strategies aimed at strengthening regional engagement are needed, including early exposure to region-based clinical training, mentorship programs with female physicians practicing in the region, and structured career guidance that emphasizes opportunities for professional growth within the region.
These results suggest that students’ demographic characteristics, academic preparedness, and ability to adapt to medical school education differ significantly depending on admission type. In particular, students with grade retention experience are more likely to encounter difficulties in various aspects, such as academic self-regulation, psychological adaptation, and utilization of effective learning strategies. To mitigate these vulnerabilities, medical schools should establish comprehensive support systems that include learning strategy development, social and emotional competency enhancement, and academic stress management programs. In addition, by establishing an early identification and intervention system for academic difficulties that meets accreditation standards, timely support can be provided and the likelihood of long-term academic underperformance can be reduced.
Third, the regional talent admission track was more likely to pursue medical careers at teaching hospitals affiliated with their alma mater or within the same region. This suggests that the regional talent track, which is increasingly adopted by non-metropolitan medical schools, may be effective in cultivating physicians who choose to remain and serve in their local communities. These findings are consistent with prior studies in United States [
3] and Japan [
6], which show that a strong alignment between students’ home regions and medical school locations is related to a higher likelihood of local physician retention.
However, some studies have noted that regional admissions alone may be insufficient, and that regional scholarship programs and community-integrated residency systems may exert a greater influence in promoting local physician retention [
6]. In addition, students who grew up in rural areas or small towns and were admitted through the Physician Shortage Area Program were found to practice in physician-shortage areas by completing clinical training in rural or small communities [
5]. Therefore, the present findings highlight the policy potential of regional talent admissions while underscoring the need for supplemental support mechanisms such as collaboration between medical schools and hospitals located in the university region (e.g., alumni mentoring programs). To enhance the effectiveness of such admissions, integrated policy frameworks that span admission, education, residency training, and long-term regional settlement are necessary. These could include scholarships or tuition waivers contingent on local service, region-based clinical placements, and community health service projects.
Several limitations should be acknowledged. First, the study was based on data from a single medical school in one region, which limits the generalizability of the findings. Therefore, additional studies involving medical schools from diverse regions and more varied student populations are needed to validate and extend these results. Second, the study did not account for the full range of admission categories used in medical school, such as academic performance-based, holistic, and essay-based admissions, which have become increasingly diverse. Further analysis of how these finer distinctions influence academic and career paths is warranted. Third, while mental health measures were collected during their pre-medical curriculum, it is unclear whether these conditions preceded medical school admission or were influenced by the academic environment. Therefore, causal inferences should be drawn cautiously.
Finally, although this study focused on the impact of admission type, variables such as student motivation, peer relationships, and faculty-student relationships may play important roles in academic performance and career paths. Therefore, further research should integrate psychological, social, and relational factors related to students.
Regardless of some limitations, this study is meaningful in that it confirms the effectiveness of the regional talent admission track, which non-metropolitan medical schools are expanding to address regional physician shortages. It also highlights the challenges faced by students who retake the college entrance exam. Also, the findings of this study are expected to serve as important foundational data for the development of future medical school admission policies and student support programs.
Data sharing statement
Please contact the corresponding author for data availability.
Acknowledgements
None.
Funding
None.
Conflicts of interest
No potential conflict of interest relevant to this article was reported.
Author contributions
Conception or design of the work: KSG, HCW. Data collection: HIS, KSG. Data analysis and interpretation: KSG. Drafting the article: KSG. Critical revision of the article: KSG, HCW. Final approval of the version to be published all authors.
Fig. 1.Latent class profiles by background characteristics. Class 1, rolling admission-regional talent group; Class 2, regular admission-male retaker group; and Class 3, non-local female group. (A) Admission type. (B) Regional talent. (C) Gap year. (D) Region-university match. (E) Sex.
Table 1.Fit Indices for Latent Class Analysis Models
Table 1.
|
Class |
Log-likelihood |
AIC |
BIC |
ABIC |
CAIC |
Entropy |
G2
|
χ2
|
|
2 |
–890.463 |
1,802.926 |
1,844.170 |
1,809.281 |
1,855.17 |
0.949 |
23.71 |
25.327 |
|
3 |
–879.892 |
1,793.784 |
1,857.523 |
1,803.604 |
1,874.523 |
0.848 |
2.567 |
2.563 |
|
4 |
–879.789 |
1,805.578 |
1,891.814 |
1,818.865 |
1,914.814 |
0.817 |
2.361 |
2.352 |
|
5 |
–878.681 |
1,815.362 |
1,924.094 |
1,832.115 |
1,953.094 |
0.752 |
0.145 |
0.145 |
|
6 |
–878.673 |
1,827.345 |
1,958.574 |
1,847.564 |
1,993.574 |
0.681 |
0.129 |
0.129 |
Table 2.Descriptive Statistics for Mental Health, Academic, and Career Variables by Latent Classes
Table 2.
|
Variable |
Class 1 |
Class 2 |
Class 3 |
Total |
p-value |
Bonferroni |
|
No. of students |
75 (23.9) |
173 (55.1) |
66 (21.0) |
|
|
|
|
Depression |
6.73±5.46 |
7.06±6.13 |
5.98±5.59 |
6.76±5.86 |
0.462 |
|
|
Suicidal ideation |
3.00±3.96 |
3.57±4.03 |
3.74±3.90 |
3.47±3.98 |
0.502 |
|
|
Internet over-dependency |
32.00±9.29 |
38.62±12.90 |
29.34±8.81 |
35.10±12.00 |
<0.001 |
1,3<2 |
|
Graduation grades |
87.45±3.67 |
85.72±3.71 |
88.63±2.67 |
86.79±3.69 |
<0.001 |
2<1,3 |
|
Dismissal |
|
|
|
|
0.768 |
|
|
No |
71 (94.7) |
164 (94.8) |
61 (92.4) |
296 (94.3) |
|
|
|
Yes |
4 (5.3) |
9 (5.2) |
5 (7.6) |
18 (5.7) |
|
|
|
Dismissal reason |
|
|
|
|
0.495 |
|
|
Transfer to another university |
3 (75.0) |
8 (88.9) |
5 (100.0) |
16 (88.9) |
|
|
|
Other |
1 (25.0) |
1 (11.1) |
0 (0.0) |
2 (11.1) |
|
|
|
Grade repetition |
|
|
|
|
0.031 |
|
|
No |
66 (93.0) |
130 (79.3) |
52 (85.2) |
248 (83.8) |
|
|
|
Yes |
5 (7.0) |
34 (20.7) |
9 (14.8) |
48 (16.2) |
|
|
|
Alma mater affiliated hospital |
|
|
|
|
0.029 |
|
|
Yes |
44 (63.8) |
71 (49.7) |
24 (40.7) |
139 (51.3) |
|
|
|
No |
25 (36.2) |
72 (50.3) |
35 (59.3) |
132 (48.7) |
|
|
|
Hospital located in the university region |
|
|
|
|
0.010 |
|
|
Yes |
46 (66.7) |
72 (50.3) |
24 (40.7) |
142 (52.4) |
|
|
|
No |
23 (33.3) |
71 (49.7) |
35 (59.3) |
129 (47.6) |
|
|
Table 3.Linear Regression of Final GPAs and Internet Over-Dependency by Latent Classes
Table 3.
|
Variable |
Predictor |
Estimate |
SE |
95% CI |
p-value |
|
Internet over-dependency |
Intercept |
32.00 |
1.35 |
29.35 to 34.65 |
<0.001 |
|
Class 2–Class 1 |
6.62 |
1.62 |
3.44 to 9.80 |
<0.001 |
|
Class 3–Class 1 |
–2.66 |
1.98 |
–6.56 to 1.24 |
0.181 |
|
Final GPA |
Intercept |
87.45 |
0.42 |
86.62 to 88.284 |
<0.001 |
|
Class 2–Class 1 |
–1.74 |
0.51 |
–2.75 to –0.728 |
<0.001 |
|
Class 3–Class 1 |
1.17 |
0.62 |
–0.05 to 2.397 |
0.06 |
Table 4.Logistic Regression of Grade Repetition and Career Paths by Latent Class
Table 4.
|
Variable |
Predictor |
Estimate |
SE |
OR |
p-value |
|
Grade repetition |
Intercept |
–2.58 |
0.46 |
|
<0.001 |
|
Class 2–Class 1 |
1.24 |
0.50 |
3.45 |
0.014 |
|
Class 3–Class 1 |
0.83 |
0.59 |
2.29 |
0.160 |
|
Hospital located in the university region |
Intercept |
–0.69 |
0.26 |
|
0.007 |
|
Class 2–Class 1 |
0.68 |
0.31 |
1.79 |
0.026 |
|
Class 3–Class 1 |
1.07 |
0.37 |
2.57 |
0.004 |
|
Alma mater affiliated hospital |
Intercept |
–0.57 |
0.25 |
|
0.024 |
|
Class 2–Class 1 |
0.58 |
0.30 |
1.97 |
0.054 |
|
Class 3–Class 1 |
0.94 |
0.37 |
2.92 |
0.010 |
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