Mental Health Status Profiles of Dentists in South Korea: A Latent Profile Analysis Approach
Article information
Abstract
Objective
Dentists encounter unique occupational challenges, including stress, depression, and anxiety, that can impact their mental well-being. Therefore, it is essential to identify dentists’ mental health statuses and the factors influencing them. Understanding the heterogeneity in dentists’ mental health is crucial for tailored interventions. To investigate different mental health profiles within the dentist population and understand the characteristics of each type.
Methods
In this cross-sectional study, we surveyed 261 Korean dentists from a sample of 1,520. Using latent profile analysis, participants were classified into distinct mental health profiles based on self-reported stress, depressive symptoms, anxiety, and sleep quality. Demographic and occupational variables were analyzed to explore their association with mental health profiles.
Results
Three distinct mental health profiles were identified: high, moderate, and low. Factors such as drinking frequency, socioeconomic status, income, and work hours significantly influenced profile classification. Significant differences in job satisfaction were observed among the profiles.
Conclusion
This study underscores the importance of personalized interventions to address the specific needs of each mental health profile, aiming to improve job satisfaction and overall mental health in the dental profession.
INTRODUCTION
Despite the significant attention that stress and mental health among dentists have received, as well as the numerous studies that have been conducted on coping strategies for such stress, dentists continue to experience high levels of stress and burnout [1]. Dentists work long hours with poor posture, and are exposed to dental procedure-related noise and the odor of dental materials [2]. Owing to the delicate nature of dental work and the need to satisfy patients’ aesthetic needs, dentists tend to be perfectionists and are easily frustrated when they cannot achieve their ideal treatment objectives [3]. Perfectionist inclinations in dentists can lead to unreasonable expectations of themselves, resulting in negative feelings, and misuse of drugs and alcohol has been documented as a troublesome coping method [4]. A British Dental Association study indicates dentists have much worse mental health than the general population [1]. In addition, a study reported that dentists experience higher levels of work-related stress than those in other jobs [5].
The mental health status of a dentist as a professional is essential in determining the quality of patient outcomes [6]. Understanding the factors influencing job satisfaction in dentists is important to ensure that patients receive high-quality care [7]. Herzberg and colleagues posited the two-factor theory, including “intrinsic motivational factors” such as recognition, work tasks, and responsibility and “extrinsic-hygiene factors” such as job security, working conditions, and salary [8]. A systematic review identified 11 factors influencing job satisfaction among dentists. Of these, six were associated with high satisfaction: patient relationships, respect, delivery of care, staff, professional relationships, and professional environment. On the contrary, five factors showed correlations with low satisfaction: personal time, stress, income, practice management, and professional time. Furthermore, the review suggested that these results may change over time, highlighting the need for longitudinal research [9]. In the UK, a change in the government’s management system negatively affected dentist job satisfaction. This was attributed to an erosion of professional autonomy rather than a mere response to changes in the remuneration system [10]. The crucial point is that dentists’ job satisfaction is intricately linked to various dimensions and cannot be solely explained by a single factor. Dentists face high occupational stress and are at a high risk of burnout. Both occupational stress and burnout are closely related to job satisfaction [8].
There have been numerous studies on the sources of work-related stress for dentists; the most commonly identified have been emergency situations, unpleasant or difficult patients, operating behind schedule, and time constraints [11]. Kay and Lowe’s [12] study examining well-being and work-related stress, highlighted patient expectations, practice management, and staffing concerns, paperwork, complaints and litigation, work relationships, clinical governance, and hours of work as contributors to work-related stress. They also found that 12% of dentists had contemplated suicide.
Occupational stress is a prevalent issue among dental professionals, largely attributed to factors such as long working hours, high patient expectations, and the demanding work environment [13]. This stress is multifaceted, encompassing stressors such as inadequate dental ergonomics, heavy workloads, extended working hours, unsatisfactory compensation, challenging interpersonal relationships with patients and colleagues, and difficulties balancing work and personal life. The culmination of these stressors can lead to burnout, characterized by a diminished interest in work, mental and emotional exhaustion, and a lack of motivation [1]. Research has linked job dissatisfaction and long working hours with higher stress levels and burnout among dentists [14]. In addition, dentists often report lower back pain as a common physical ailment, a direct consequence of their extensive workloads [14].
Persistent stress, burnout, and mental health problems will likely negatively impact dental practitioners’ well-being [15]. Impaired well-being or burnout has been linked to a decline in the quality of healthcare delivery. Studies have found a link between mental health disorders, including burnout and depression, and the likelihood of reporting medical errors, implying that degraded practitioner well-being can negatively impact patient safety [15]. Similar findings have been observed in the field of dentistry, indicating that an increase in depersonalization scores is linked to the perception of dental errors [16]. Conversely, higher levels of personal success, as measured by burnout assessments, appear to have a protective impact [16]. Therefore, to enhance the quality of dental care, it is necessary to examine the mental health of dentists, whose jobs are associated with high stress levels.
There is significant heterogeneity in the way individuals perceive and react to stress. In an existing stressful context, individuals might show varying emotional responses. Dentists may manifest symptoms of depression, anxiety, sleeping disorders, or a combination of these. Conventional statistical methodologies have successfully determined the influence and correlation of individual factors, such as depression, anxiety, and sleep. However, it is important to acknowledge that the various components of an individual’s psychological well-being are diverse and complex [3].
Latent profile analysis (LPA) is a statistical technique used to identify unobserved subgroups or profiles within a population based on responses [17]. Recently, researchers have been utilizing LPA to comprehend individual differences and the manifestation of symptoms, making it ideal for studying psychopathology [18]. For example, in a study of mental health profiles of women at high risk for postpartum depression using LPA, the three profiles identified showed significant differences in emotion management, psychological flexibility, maternal self-efficacy, and partner relationship satisfaction, highlighting the existence of distinct patterns of mental health symptoms among women at high risk for postpartum depression [19]. This finding highlights the importance of considering symptoms for personalized prevention and treatment strategies [19].
There is no LPA of this person-centered approach in the literature on mental health and job stress among dentists. Therefore, we aimed to investigate different mental health profiles within the dentist population and understand the characteristics of each type. By identifying the characteristics and associated factors of each type, it may be possible to provide individualized mental healthcare or support.
Research questions
1. How many profiles of dentists’ mental health and job stress are classified, and what are the characteristics of those profiles?
2. Which determinants statistically affect dentists’ mental health and job stress profile classification?
3. Are there any differences in job satisfaction among dentists’ mental health and job stress profiles?
METHODS
Sample
We used survey data collected from 1,520 Korean dentists with the aid of local branches of the Korean Dental Association. Messages linked to the online questionnaire site were sent twice from September to November 2016. Among the 1,520 participants, 261 dentists formed the final sample after excluding those with inadequate responses or who did not sign the consent form. This study was conducted in accordance with the Declaration of Helsinki and received ethical approval from the Dankook University Institutional Review Board (IRB No. 2016-07-024-002). Written informed consent was obtained from all participants.
Assessment
Sociodemographic and general information
Participants’ sex, religion, socioeconomic status (SES), income, exercise frequency, drinking, smoking, marital status, occupation, work hours, job satisfaction, and job status prospect information was collected. Sex and religion were dummy coded; 1 indicated being a woman and having a religion. SES and income were measured on three levels (low, middle, and high). Exercise frequency was measured as never, 1–2 times, 3–4 times, and ≥5 times per week. Drinking and smoking were based on the current time point. Drinking was measured as never, ≤3 times per month, 1–2 days per week, and ≥3 days per week, and smoking was dummy coded as yes and no. Marital status was classified as unmarried, married, divorced, separated, or bereaved and dummy coded as 1=married and 0=other for ease of analysis. Occupational status was categorized as employed dentist, private outpatient clinic dentist, and others. For analysis, two dummy variables were created. In the first dummy, private outpatient clinic dentist was the reference group and employed dentist was coded 1. In the second dummy, private outpatient clinic dentist was the reference group and others (residents, public health dentists, military dental officers, etc.) were coded 1. The average working hours were recorded as on leave, 4–8 h, 8–10 h, and >10 h. Job satisfaction was recorded on a scale of 1=dissatisfied to 3=satisfied. Detailed information is depicted in Table 1.
Descriptive statistics of all variables used in this study
Psychological assessments
General stress, occupational stress, dentist stress, depression, anxiety, and sleep were measured to assess various domains of the participants’ mental health. General stress was evaluated by the Korean version of the Brief Encounter Psychological Instrument (BEPSI-K) [20]. The BEPSI-K is used to measure sociopsychological stress in different situations on a 5-point Likert scale. The scale is based on the past month, for example, “In the past month, have you felt mentally or physically overwhelmed with the challenges of living?” Occupational stress was measured by the abbreviated Doctor Job Stress Scale [21]. The scale has three sub-factors: job factors, patient factors, and clinical responsibility and judgment. Examples items include, “I feel tired at the end of the day,” “Many patients are difficult to deal with,” and “I have to make decisions that could have a serious impact on a patient’s condition.” Dentist stress was measured using the newly designed Dentist Job Stress questionnaire [5]. This questionnaire is a self-report measure of stress as a dentist, with six items rated on a 5-point Likert scale. It has two subfactors: environmental and vocational. An example of an environmental item is, “I do not feel comfortable in my practice,” while an example of a vocational item is, “I would choose a different career if I had the opportunity.” Depressive symptoms were measured using the Korean version of the Center for Epidemiologic Studies Depression Scale (CES-D) [22]. The CES-D has 20 questions and is measured on a 5-point Likert scale. Anxiety was evaluated using the Korean version of the State-Trait Anxiety Inventory (STAI-S) to score state anxiety [23]. The STAI-S is a 20-item, 4-point Likert scale measuring current situation-related anxiety. Finally, sleep was assessed using the Korean version of the Pittsburgh Sleep Quality Index (PSQI) [24]. The PSQI is a 19-item questionnaire that assesses seven components of sleep quality, including the sleep hours and awake time in the past two weeks and the level of interruption from sleep distractions (4-point Likert scale). A higher score indicates a poor psychological state on all scales.
Data analysis
LPA for mental health status
We used LPA to identify dentists’ mental health subgroups. The conventional variable-centered approach (e.g., multiple regression) assumes the homogeneity of the sample and analyzes variables in sum. On the contrary, a person-centered approach, such as LPA, captures the sample’s heterogeneity by classifying subgroups based on similar response patterns and exploring the characteristics of each group.
In LPA, the optimal number of subgroups is determined by increasing the number of groups. At this time, the information criteria (Akaike information criterion [AIC], Bayesian information criterion [BIC], sample-size adjusted Bayesian information criterion [SABIC]), classification quality (entropy), and model comparison verification (Lo–Mendell–Rubin likelihood ratio test [LMR-LRT] and bootstrapped likelihood ratio test [BLRT]) were checked, and the most suitable number of latent profiles was determined considering interpretability. The smaller the AIC, BIC, and SABIC values, the more appropriate the model [25,26]. Entropy appears between 0 and 1, and a larger value (>0.8) indicates a clearer group classification [27]. The adjusted LMR-LRT [28] and parametric BLRT [29] were used to evaluate the relative fit comparisons. If the p-values of the LMR-LRT and BLRT were significant, the k-1 model was rejected, and the latent profile model classified as k was selected [28,30].
After determining the latent profiles, the significance of the determinants was examined using a three-step approach [31]. The previously used one-step approach simultaneously analyzed latent profile classification and determinants, resulting in significant changes in latent profile classification. On the contrary, the three-step approach classifies the latent profiles and estimates the effect of determinants separately so that profile estimation does not change, even if the outcome variable or predictor changes [31]. We used SPSS 25 (IBM Corp., Armonk, NY, USA) for descriptive statistical analysis and Mplus 8.3 [32] for LPA. The characteristics of all variables used in the analysis are presented in Table 1. The kurtosis of all variables did not exceed 2, and the skewness did not exceed 4 [33]. Therefore, normality of the variables was not a problem.
RESULTS
Determination of the optimal number of dentists’ mental health profiles
Prior to the LPA, owing to the different scales of the dentists’ mental health questions, scores were standardized to compare them at the same level. Next, to determine the optimal number of latent groups, statistical determination standards were compared as the number of latent groups increased from two to five (Table 2). First, information criteria, including the AIC, BIC, and SABIC, get lower as the number of latent profiles increases. A lower information index indicates a better model fit, but in general, the information criteria tends to decrease as the number of latent classes increases and becomes more complex. In this case, the point where the decrease changes most sharply can be considered as the optimal number of latent profiles, which is called the elbow point, as presented in Figure 1 [34]. In this study, we found that the information index decreases the most when the number of latent profiles increases from two to three. When checking the entropy, all models’ entropy was higher than 0.8, which means the classification quality of all groups was good. Furthermore, the BLRT was p<0.001 among model comparison tests, making it difficult to determine the number of latent profiles. However, the LMR-LRT showed insignificant p-values at four- and five-profile models. Therefore, three groups were determined as the optimal number of latent profiles based on interpretability, simplicity of the model, and elbow point of the information criteria.
Model fit statistics with different numbers of profiles
Information criteria as the number of latent profiles increase (elbow point was circled). AIC, Akaike information criterion; BIC, Bayesian information criterion; SABIC, sample-size adjusted Bayesian information criterion.
Characteristics of dentists’ mental health profiles
The final three groups of dentists’ mental health profiles are presented in Figure 2. Note that a higher score indicates a bad psychological state on all scales. The first class was named the “low mental health status” group because all mental health scores, including general stress, doctor stress from patient and responsibilities, dentists’ stress, depressive symptoms, anxiety, and low quality of sleep, were higher than other groups, except for the stress from work as doctors. The second group was named the “moderate mental health status” group because all mental health scores were moderate compared to the other two groups. The final group showed the lowest mental health score and was named the “high mental health status” group. Of the sample, 17.9%, 46.1%, and 36.0% of dentists were included in the low, moderate, and high mental health status groups, respectively.
Latent profiles of each mental health group.
Examining the determinants of dentists’ mental health profiles
When examining the significance of the factors influencing latent profile classification more frequent drinkers were more likely to belong to the moderate or low than the high mental well-being group. Next, when comparing the high and moderate groups, participants with lower subjective SES and longer working hours were more likely to be in the moderate rather than the high group. When comparing the high and low groups, participants with lower subjective income, who were not married, and who were private clinic dentists (compared to other groups) were more likely to be in the low than the high group. No variables significantly affected the distinction between the moderate and low groups. In addition, sex, religion, exercise, smoking, and being an employed dentist had no significant effect on any group distinctions. All results are presented in Table 3. As expected, job satisfaction was highest in the high mental health status group, followed by the moderate and low groups. All group mean differences were statistically significant (Table 4).
Examining statistical significance of determinants influencing the classification of latent profiles
Test of the differences in job satisfaction among mental health status groups
DISCUSSION
We classified dentists’ mental health statuses into three latent profile types. Furthermore, we examined the significant influence of various factors, including sex, religion, SES, income, exercise, drinking, smoking, marital status, occupational status, and work hours, on these profiles. In addition, we analyzed differences in dentists’ job satisfaction based on these latent profile types.
Based on the LPA, the mental health types were classified into three groups: high, moderate, and low. Each group displayed specific characteristics and stressors. The low mental health status group, constituting 17.9% of the sample, showed high levels to all stressors except for the stress from working as a doctor, indicating the worst mental health. Conversely, the high mental health status group comprised 36.0% of the sample and exhibited lower level to general stress. The moderate mental health status group, accounting for 46.1%, had intermediate mental health levels compared to the other two groups. The latent profile pattern showed greater group differences in mental health, such as depressive symptoms, anxiety, and sleep, than in stress, such as general stress, doctor job stress, and dentist job stress. Overall, groups were classified based on the intensity of stress and mental health, implying that dentists experience stress across various domains simultaneously. One study argues that such stress affects the functioning of psychological processes and ultimately influences psychiatric disorders such as depressive symptoms, thus underscoring the need for research and intervention on psychological factors such as stress [35]. Therefore, comprehensive interventions addressing the overall living environment and mental health are crucial, followed by treatment based on the analyzed influencing factors.
Drinking was found to be a significant factor influencing latent profiles, with frequent drinking increasing the likelihood of being in the medium or low mental health group rather than the high mental health group. This result is similar to the previous finding, which found that alcohol use has a significant correlation with the level of work-related stress [2]. Dentists tend to have perfectionist tendencies and are often exposed to unrealistic expectations, leading to negative emotions and misuse of alcohol and drugs [4,36]. The Canadian Dental Association explained that dentists’ reliance on alcohol and drugs for coping is due to high stress, unrealistic perfectionistic expectations, vulnerability in the clinical setting, stressful environment, long working hours, and excessive professional demands [4]. The relationship between drinking and work-related stress is not only evident in dentists but also in other professions. Firefighters and police officers were found commonly resort to drinking as a means of stress relief [37]. In addition, domestically, 17.4% of firefighters and 24.0% of police officers engage in drinking owing to work-related stress [37].
Drinking-related theories include the tension-reduction hypothesis, suggesting that people drink to alleviate depressive symptoms, and the toxicity hypothesis, indicating that drinking can lead to depressive symptoms [37,38]. Binge drinking is linked to a 2–4 times higher risk of depressive symptoms and increased suicidal thoughts [37].
Longer working hours increased the likelihood of belonging to the moderate mental health status group rather than the high mental health status group. Many studies have identified working hours as a source of stress for dentists, and the long hours, owing to the nature of dental work, can lead to physical ailments such as lower back pain and musculoskeletal disorders [1,13,14,39]. However, Mathias et al. [40] found no correlation between working hours and depressive symptoms among dentists, suggesting that perceived workload intensity may not directly correlate with mental well-being. As indicated by previous studies, dentists’ occupational stress stems more from the content of their clinical work, cooperation from staff and patients, relationships with patients, attention to detailed tasks, and the necessity of making decisions for treatment and operations rather than merely from the burden of the work itself or long working hours.
A lower income level has been found to increase the likelihood of belonging to the low mental health status group compared to the high mental health status group. Income level primarily reflects the monetary earnings that an individual or household receives. It serves as a direct indicator of economic stability and plays a crucial role in mitigating economic stress and financial difficulties. For instance, lower income levels are associated with greater economic instability, which can negatively impact psychological stress and job satisfaction.
Furthermore, lower SES has been identified as a factor that increases the likelihood of belonging to the moderate mental health status group rather than the high mental health status group. SES is a comprehensive concept that includes not only income but also education level, occupational status, and other economic resources. SES is utilized as a holistic measure to evaluate an individual’s economic and social position within society. Subjective SES reflects individuals’ perceptions of their education and occupational status, often in relation to social comparison. This concept resonates with the idea that social capital is intertwined with psychological capital, influencing subjective well-being [41].
Dentists in private practice were more likely to belong to the low mental health status group compared to those in training or other forms of practice. This suggests that private practitioners face unique mental health challenges, possibly owing to the burden of making treatment decisions, responsibility for treatment outcomes, managing a practice, and adapting to competitive and financial pressures.
Considering that there were few dentists in private practice among those who perceived themselves as having a low SES or felt that their income was lower than expected, it can be speculated that there may be different factors related to mental health and other aspects for dentists in private practice compared to those who undergo training at university hospitals or pursue other forms of dental practice. Possible factors that can be responsible for this include the burden of making decisions to restore patients’ oral health, as well as the substantial responsibility for treatment outcomes in the clinical aspect. Beyond clinical aspects, dentists may also have to adapt quickly to changes related to hospital management, intense competition, medical fees, rising labor and material costs, and the consequent increase in fixed expenses.
The finding that unmarried dentists were more likely to belong to the low mental well-being group when compared to the high mental well-being group aligns with previous study results, which also found that unmarried individuals tend to experience more depressive symptoms than their married counterparts [40]. This is consistent with other studies showing that marital status and family support can reduce burnout [42].
We found significant differences in job satisfaction across the three mental health status groups. Dentists with higher mental health status reported greater job satisfaction, which could potentially improve the quality of dental care and patient outcomes. This emphasizes the importance of focusing on dentists’ mental health status to enhance job satisfaction and, consequently, the quality of dental care.
Excessive work stress, burnout, and dissatisfaction are related to healthcare professionals’ job and career turnover, and it can be speculated that such professional dissatisfaction is associated with increased turnover rates and rising costs related to physician recruitment and retention [6]. Overall, job satisfaction among physicians can positively impact patient treatment compliance and chronic disease management behavior. A narrative review of the consequences of job dissatisfaction suggests that dissatisfied physicians may make riskier prescriptions, and when patient compliance is low, there tends to be lower patient satisfaction, all of which can lead to a deterioration in the quality of patient care [43]. On the contrary, Jones et al. [44] suggested that stress management interventions for physicians can be beneficial for both doctors and patients, and their results demonstrated a close relationship between high levels of stress in medical departments and hospitals and the risk of medical errors.
In the present study, job satisfaction showed a significant mean difference in the order of high, moderate, and low among the three mental health status groups. This implies that dentists’ mental health status level is closely related to their job satisfaction, consistent with the findings of other studies mentioned earlier. Higher job satisfaction may lead to an improvement in the quality of dental care and better patient outcomes. More attention should be paid to the mental health status of dentists and efforts should be made to improve it in order to maintain high levels of job satisfaction.
This study highlights the mental health challenges faced by dentists, but several limitations point to directions for future research. First, although drinking is a significant factor influencing mental health issues such as depressive symptoms, establishing a causal or precedential relationship is challenging due to varying criteria for assessing drinking levels across different studies. Future research should standardize these criteria, particularly regarding the amount and frequency of binge drinking. Second, our categorization of marital status as either currently married or not could not differentiate between unmarried, divorced, separated, or widowed individuals. This lack of specificity may mask the diverse influences of marital status on mental health. Future studies should consider these variations to better understand the role of marital support and family dynamics in coping with stress. Third, our study did not account for the distribution of dentists by occupation type or factors related to the size of the workplace or training post-licensure. Research is needed to explore these aspects, including the impact of career length and additional training on work-related stress and mental health. Moreover, the study did not encompass social factors affecting dentists’ mental health, which can vary significantly due to differences in social and healthcare systems across countries. Future research should prioritize the perspectives of dentists themselves to gain a deeper understanding of stress factors specific to their practice environment. Investigating how domestic dentists cope with stress in their unique societal settings is crucial for proposing effective, tailored stress management strategies.
Despite the limitations mentioned earlier, this study’s strength lies in its utilization of LPA, which assumes that individuals may belong to different latent groups rather than assuming that people within a given profession share identical characteristics. Unlike previous research that may have assumed dentists as a homogenous group when examining correlations between variables such as stress, depressive symptoms, anxiety, and sleep, we adopted a person-centered approach, considering the heterogeneity among individual dentists. In addition, by exploring the factors influencing the latent profile classification of dentists and verifying the differences in job satisfaction among these profiles, this study underscores the need for mental healthcare tailored to dentists. The aforementioned LPA study on postpartum depression suggested clinical applications such as enhanced screening protocols, targeted interventions, and web-based support programs [19]. Based on the results of this study, it is possible to consider the personalized application of the results in clinical practice considering the characteristics of Korea. For dentists in private practice who may be psychologically vulnerable, the Korean Dental Association could provide a toolkit for more proactive management of mental health conditions. It may also be useful to provide educational programs during dental school and specialty training on practical approaches to active mental health management, including time management and supportive personal relationships. As this study was conducted among Korean dentists, conducting studies in other countries may be necessary to identify differences between countries, discuss the evidence base and practical clinical applications of mental health care for dentists internationally, and respond in solidarity.
Notes
Availability of Data and Material
Data supporting the findings of this study are available upon request from the corresponding author. The data are not publicly available because of privacy concerns.
Conflicts of Interest
The authors have no potential conflicts of interest to disclose.
Author Contributions
Conceptualization: Chisung Yuh, Sojin Yoon, Chul-Hyun Cho. Data curation: Chisung Yuh, Sojin Yoon, Kyungwon Song, Chul-Hyun Cho. Formal analysis: Chisung Yuh, Sojin Yoon, Chul-Hyun Cho. Funding acquisition: Chul-Hyun Cho. Investigation: Chisung Yuh, Sojin Yoon, Kyungwon Song, Chul-Hyun Cho. Methodology: Chisung Yuh, Sojin Yoon, Chul-Hyun Cho. Project administration: Chisung Yuh, Sojin Yoon, Kyungwon Song, Chul-Hyun Cho. Resources: Chisung Yuh, Sojin Yoon, Kyungwon Song, Chul-Hyun Cho. Software: Chisung Yuh, Sojin Yoon, Chul-Hyun Cho. Supervision: Heon-Jeong Lee, Young-Mee Lee, Chul-Hyun Cho. Validation: Chisung Yuh, Sojin Yoon, Young-Mee Lee, Chul-Hyun Cho. Visualization: Chisung Yuh, Sojin Yoon, Chul-Hyun Cho. Writing—original draft: Chisung Yuh, Sojin Yoon, Young-Mee Lee, Chul-Hyun Cho. Writing—review & editing: Chisung Yuh, Sojin Yoon, Young-Mee Lee, Chul-Hyun Cho.
Funding Statement
This work was supported by National Research Foundation of Korea (NRF) grants funded by the Ministry of Science and Information and Communications Technology (MSIT), Government of Korea (NRF-2021R1A5A8032895); Information and Communications Technology and Future Planning for Convergent Research in the Development Program for R&D Convergence Over Science and Technology Liberal Arts (NRF-2022M3C1B6080866); an Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean government (MSIT; RS-202300224823); and a Korea University Grant.
Acknowledgements
None