Predicting Turnover Intention Among Newly Graduated Nurses in South Korea: A Decision Tree Analysis

Predicting Turnover Intention Among Newly Graduated Nurses in South Korea: A Decision Tree Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predicting Turnover Intention Among Newly Graduated Nurses in South Korea: A Decision Tree Analysis Mikyung Moon, Hyunwook Kang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7082239/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Nov, 2025 Read the published version in BMC Nursing → Version 1 posted 12 You are reading this latest preprint version Abstract Background. The high turnover intention of newly graduated nurses has a multifaceted impact on the healthcare system. Analyzing large datasets using the machine learning methods can more accurately predict influencing factors of turnover intention in this population. This study aims to identify predictors of turnover intention among newly graduated nurses with less than one year of experience in current workplaces, using a decision tree model by analyzing 2016-2020 Graduate Occupational Mobility Survey conducted in South Korea. Methods. This is a secondary data analysis using a national large dataset. The data of 492 new nursing graduates were included in the analysis. Predictive variables for modeling were grouped into four categories: personal factors, workplace factors, college factors, and physical and mental health factors. Among these, the variables identified through univariate analysis were selected for the final analysis. The Chi-square Automatic Interaction Detection decision tree algorithm was implemented using SPSS Modeler. Results. 23.6% (N=116) of participants reported turnover intention. The key predictors of turnover intention included lower levels of job satisfaction concerning personal development and social reputation related to the job, as well as the absence of incentive payments. Factors associated with a high intention for retention included greater satisfaction with personal growth and promotion systems, employment in permanent positions, holding full-time jobs, and experiencing fewer feelings of listlessness. Conclusion: Nursing administrators must endeavor to develop effective human resource management strategies that offer opportunities for self-development and career advancement, improve the social reputation of the institution, and ensure job security to mitigate early turnover intentions among newly graduated nurses. Additionally, integrating mental health management is crucial for enhancing workforce stability. In nursing colleges, developing educational strategies to prepare nursing students for organizational expectations and nursing competence will contribute to improving retention within this population. Clinical trial number: not applicable. decision trees nurses personnel turnover secondary data analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Background The adequate nurse-patient ratio is essential for maintaining quality nursing care and maximizing patient outcomes. However, the global nursing shortage has become a significant concern. South Korea is notorious for its low nurse-patient ratio which is likely attributed to high turnover rates. As of the year 2000, the number of licensed nurses working in clinical settings in South Korea is significantly smaller than that in the Organization for Economic Co-operation and Development (OECD) countries (4.4 vs. 8.0 per 1,000 population), while the number of new nursing graduates is greater than in OECD countries (42.4 vs. 31.4 per 100,000 population) [ 1 ]. The turnover rate of Korean nurses is approximately three times higher than that of nurses in the United States as of 2016 [ 2 ]. These results strongly indicate nursing graduates in South Korea are likely to remain in their positions only for a brief duration. High turnover rates among nurses have a considerable negative impact on patient outcomes, including lower activities of daily living, worsening existing pressure ulcers or developing new ones, prolonged hospitalization, and increased medical errors [ 3 , 4 ]. Consequently, nurses’ high turnover contributes to increasing healthcare costs [ 3 ]. It was reported that the turnover rate of newly graduated nurses (NGNs) is higher than that those with more than five years of working experience [ 5 ]. NGNs are often experience a substantial level of job-related stress during their short transition period from students to competent nurses, which lasts for a couple of years after graduation [ 6 ]. They are required to adapt to their new roles as professional healthcare personnel, learn clinical knowledge and build relationships with various types of colleagues. Dissatisfaction with job experiences and difficulty in adapting to their new roles may contribute to high turnover rates. Lee [ 6 ] reported that 25% of nurses left their job within one year after graduation. Turnover intention is defined as “the thoughts and behaviors among organizational members to voluntarily leave the organization” [ 7 ] and it is a strong predictor of actual turnover among nurses [ 8 ]. One study reported that approximately 20% of nurses expressing turnover intention ended up leaving their positions [ 8 ]. Turnover intention is considered a complex concept that involves economic, psychological, and organizational outcomes, all of which depend on the interaction of various factors [ 9 ]. Therefore, numerous studies have identified correlates of turnover intention among NGNs using traditional statistical methods. Factors related to NGNs’ turnover intention include work schedules, the desirability of the hospital, opportunities for professional development, becoming a part of a team and availability of practical support [ 10 ]. Individual factors include younger age, male gender, higher levels of education, marital status, and lower work experience [ 11 , 12 ]. A more recent study reported that NGNs’ turnover intention was 12.8% [ 5 ]. This study identified significant associations between turnover intention and factors such as higher job stress, sleep disturbances, poor subjective health status, and working in intensive care units. Job satisfaction has consistently been identified as a strong predictor of both turnover intention and actual turnover [ 11 , 12 ]. Among NGNs, the initial job experience is often described as a period of transition or adaptation. Studies on NGNs’ turnover have reported that transitional shock, which is characterized by the gap between learned theory and practical application, overwhelming workloads, lack of social support, and conflicts with coworkers, significantly contributes to turnover intention [ 13 , 14 ]. These findings suggest that a wide range of factors contribute to the turnover intention of NGNs. Therefore, accurately understanding this phenomenon is crucial for maximizing organizational efficacy and enhancing the quality of nursing care, which is directly linked to positive patient outcomes. However, most of these studies have limitations due to small sample sizes and geographical restrictions. In particular, since most studies have verified only a limited number of variables selected by researchers, it is difficult to comprehensively identify the influencing factors as a whole [ 15 ]. Accurately predicting turnover intention using large datasets that incorporate multifaceted factors is crucial for developing more effective strategies to increase retention rates of NGNs. Analyzing large datasets using the machine learning methods can more accurately predict influencing factors by considering the interactions and nonlinear relationships among various independent variables [ 16 ]. In particular, decision tree models, which are a type of machine learning algorithm, provide a valuable analytical framework for predicting related factors by identifying meaningful patterns and rules. These models have demonstrated effectiveness in predicting relevant factors to support decision-making in healthcare settings, and often outperformed traditional methods in specific contexts [ 17 , 18 ]. Specifically, the Chi-square Automatic Interaction Detection (CHAID) decision trees employ the chi-square test of association to assess the goodness of fit between observed and expected outcomes, thereby determining splitting rules and node formation. The CHAID decision tree has a unique tree structure that can generate non-binary trees, which means some splits may have more than two branches, making it particularly suitable for analyzing larger datasets [ 19 ]. The purpose of this study is to identify factors influencing on turnover intention among NGNs with less than one year of experience in the current workplaces in South Korea, using a decision tree model based on the data from the 2016–2020 Graduate Occupational Mobility Survey (GOMS). Methods Study Design and Data Source This study conducted a secondary analysis using the recent five years (2016 to 2020) of data from the GOMS, produced by the Korea Employment Information Service (KEIS). The GOMS is a nationally representative survey of newly graduated college students in Korea [ 20 ]. The GOMS sample comprises approximately 4% of all annual number of college graduates in Korea, with sampling allocation based on academic majors. The survey is conducted annually between September and November of each year following graduation (18 to 24 months after college completion). GOMS includes demographic characteristics and a wide range of job-related variables to analyze the job-related mobility of college graduates in Korea. Study Sample From the GOMS data from 2016 to 2020, 1,555 nursing graduates were identified. Among these, 205 NGNs who were not working at the time of the survey were excluded. Based on the purpose of this study, the final study sample comprised 492 NGNs with less than one year of experience at their current workplaces (Fig. 1 ). Measures Outcome Variable To assess turnover intentions among NGNs, we used a GOMS item that asked whether participants were actively preparing to change jobs. This variable was measured using a binary scale. Predictive Variables To examine the factors influencing turnover intentions among NGNs, a total of 110 variables were categorized into four groups: personal factors, current job-related factors, colleged-related factors, and physical-mental health related factors. The personal factors included six demographic characteristics: age, gender, marital status, living arrangement, average monthly parental income, and parents’ current assets. The current job-related factors comprised 42 items, including part-time status, modes of transportation to the workplace, commuting time, shift work status, benefits provided by the current workplace, permanent employment status, internship experience, regular working days, regular working hours and overtime hours per week, average number of working days on holidays, and average monthly income, as well as 15 subdomains of job satisfaction rated from 1 (very dissatisfied) to 5 (very satisfied). The college-related factors included 27 items related to reasons for selecting the college and major, participation in career preparation programs, satisfaction with various aspects of the college experience, grade point average, items related to student loans, and career plans prior to graduation. Finally, the physical and mental health-related factors comprised 20 items, including current subjective health status (1 = not very heathl ~ 5 = very healthy), current health behaviors, physical limitations, items on life satisfaction (1 = not at all ~ 7 = very much so), and items assessing emotional experiences over the past month rated from 1 (did not feel at all) to 7 (always felt it). Data Analysis Means, standard deviations, and frequency were calculated to describe characteristics of participants using the IBM SPSS statistics for Windows version 29. To reduce the risk of overfitting and enhance the predictive performance of the model, univariate analyses using chi-square tests and independent t-tests were conducted. As a result, a total of 45 significant variables were identified and included in the final decision tree model. Decision tree analysis with the CHAID algorithm was conducted to identify factors influencing turnover intention among NGN’s using IBM SPSS modeler 17.0. The dataset was randomly divided into a training set (70%) and a testing set (30%), which is commonly used ratio for predictive modeling. For missing values, decision trees categorized them as separate branches in each split [ 18 , 21 ]. The relative importance of each predictor was determined on a scale from 0 to 1. The values are derived from sensitivity measures of the predictors, reflecting the amount of variance in the target variable that is explained when the value of a specific predictor is known. A higher score indicates greater importance of the predictor [ 21 ]. Model performance was evaluated using accuracy and the area under the receiver operating characteristic (ROC) curve (AUC). An AUC value between 0.70 and 0.80 is interpreted as indicating moderate discriminative ability, while a value above 0.80 is considered indicative of strong discriminative performance [ 22 ]. Results Characteristics of Participants The demographic characteristics of the participants are presented in Table 1 . The mean age of the participants was 25.7 ± 3.8 years and 86.0% were women. The majority of them (96.1%) was single. Of the total participants, 116 individuals (23.6%) reported an intention to leave their current job positions. Table 1 Demographic characteristics of participants (N = 492) Variable n (%) or (M ± SD * ) Age (year) 25.7 ± 3.8 Gender Female 423 (86.0) Male 69 (14.0) Marital status Single 473 (96.1) Married 19 (3.9) Living with parents Yes 241 (49.0) No 251 (51.0) Average monthly income (million won) 248.6 ± 65.8 Subjective health status 3.5 ± 0.9 * M = Mean; SD = Standard Deviation Descriptive characteristics of predictors are shown in Supplementary 1. Regarding current job-related factors, 93.1% were employed full-time, and 62.8% were engaged in shift work. Among employee benefits, legal severance pay (89.4%) and incentive payments (82.7%) were the most commonly provided, while paid weekly holidays (60.6%) was the least provided. The majority (79.1%) were permanent employees, and the average monthly wage was 2.49 million won. In terms of satisfaction in the current job, the participants were the most satisfied with employment security (3.8 ± 0.9) and were the least satisfied with salary or income (3.1 ± 1.0). Satisfaction with social reputation for their jobs and the promotion system was reported as 3.6 ± 0.9 and 3.2 ± 0.9, respectively, on a 5-point Likert scale. Regarding nursing college factors, the primary reason for selecting a nursing major was promising job and employment prospects (62.0%). Among career selection and job preparation activities, 52.4% participated in aptitude tests or other career psychometric assessments, and 44.9% attended courses related to career and job preparation. Participants reported the highest satisfaction with the curriculum and educational content in their nursing schools (3.4 ± 0.9) and the lowest satisfaction with educational support facilities. Regarding physical and mental health factors, the average score for subjective health status was 3.5 ± 0.9 on a 5-point scale. Among emotional experiences reported during the past month, “joyful” was the most frequently experienced emotion (4.9 ± 1.3), while “listless” was the least frequently experienced (3.6 ± 1.8). Univariate Relationships Between Predictors and NGNs’ Turnover Intention 30 current job-related factors, five college-related factors, and ten physical and mental health-related factors showed statistically significant associations with nurses’ turnover intention (Table 2 ). Table 2. Univariate relationships between predictors and newly graduated nurses’ turnover intention (N=492) Variable Total n (%) or M ± SD * Turnover intention n (%) or M ± SD * χ 2 or t ( p ) Yes (n = 116) No (n = 376) Current job-related factors Part-time employment status Yes 34(6.9) 20(17.2) 14(3.7) 25.18 (< .001) No 458 (93.1) 96 (82.8) 362 (96.3) Shift work status Yes 309 (62.8) 62 (53.4) 247 (65.9) 6.03 (.014) No 181 (26.9) 54 (46.6) 127 (22.9) Provided benefit Legal severance pay Yes 440 (93.8) 93 (87.7) 347 (95.6) 8.73 (.003) No 29 (6.2) 13 (12.3) 16 (4.4) Paid vacation Yes 401 (88.3) 84 (82.4) 317 (90.1) 4.55 (.033) No 53 (11.7) 18 (17.6) 35 (9.9) Overtime pay Yes 376 (81.0) 77 (73.3) 299 (83.3) 5.24 (.022) No 88 (19.0) 28 (26.7) 60 (16.7) Incentive payment Yes 407 (85.9) 83 (74.8) 324 (89.3) 14.69 (< .001) No 67 (14.1) 28 (25.2) 39 (10.7) Paid weekly holiday Yes 298 (78.2) 51 (65.4) 247 (81.5) 9.48 (.002) No 83 (21.8) 27 (34.6) 56 (18.5) Permanent employment status Yes 389 (79.4) 76 (65.5) 313 (83.7) 17.87 (< .001) No 101 (20.6) 40 (34.5) 61 (16.3) Average monthly income (million won) 248.6 ± 65.8 227.0 ± 74.8 255.2 ± 61.4 3.69 (< .001) Job satisfaction with Salary or income 3.1 ± 1.0 2.7 ± 1.1 3.3 ± 0.9 5.08 (< .001) Employment security 3.8 ± 0.9 3.5 ± 1.1 3.9 ± 0.8 3.86 (< .001) Working environment 3.6 ± 0.9 3.4 ± 1.1 3.7 ± 0.9 2.71 (.007) Working time 3.3 ± 1.1 3.1 ± 1.3 3.4 ± 1.1 2.27 (.024) Personal growth 3.4 ± 1.0 2.9 ± 1.1 3.5 ± 0.9 6.03 (< .001) Interpersonal relationship 3.5 ± 1.0 3.3 ± 1.2 3.6 ± 0.9 3.03 (.003) Employee benefits 3.4 ± 1.0 3.0 ± 1.0 3.5 ± 0.9 5.11 (< .001) Promotion system 3.2 ± 0.9 2.7 ± 0.9 3.3 ± 0.9 6.52 (< .001) Social reputation for one’s job 3.6 ± 0.9 3.1 ± 0.9 3.7 ± 0.8 6.46 (< .001) Autonomy and authority 3.4 ± 1.0 3.0 ± 1.1 3.5 ± 0.9 4.23 (< .001) Social reputation for workplace 3.6 ± 0.9 3.3 ± 0.9 3.7 ± 0.8 4.33 (< .001) Match between job and personal interests 3.5 ± 0.9 3.1 ± 1.0 3.6 ± 0.9 5.84 (< .001) Job-related education or training 3.5 ± 0.9 3.0 ± 0.9 3.6 ± 0.8 6.89 (< .001) Overall for current job 3.4 ± 0.9 3.0 ± 1.0 3.5 ± 0.8 6.41 (< .001) Overall for main duties 3.4 ± 0.9 3.1 ± 1.0 3.5 ± 0.8 4.51 (< .001) Job-educational level match 3.1 ± 0.7 2.8 ± 0.9 3.1 ± 0.7 3.19 (.002) Job-skill level match 3.1 ± 0.7 2.9 ± 0.8 3.1 ± 0.6 2.29 (.024) Job-academic major match 3.9 ± 1.0 3.5 ± 1.2 4.1 ± 0.9 4.61 (< .001) Enrollment status in the current workplace Health insurance Yes 456 (92.7) 354 (94.1) 102 (89.7) 6.66 (.036) No 17 (3.5) 5 (4.3) 12 (3.2) Unaware 19 (3.9) 9 (7.8) 10 (2.7) Employment insurance Yes 432 (87.8) 94 (81.0) 338 (89.9) 9.07 (.011) No 29 (5.9) 8 (6.9) 21 (5.6) Unaware 31 (6.3) 14 (12.1) 17 (4.5) Worker’s compensation insurance Yes 407 (82.7) 87 (75.0) 320 (85.1) 8.35 (.015) No 25 (5.1) 6 (5.2) 19 (5.1) Unaware 60 (12.2) 23 (19.8) 37 (9.8) College factor Participation in career selection and job preparation program Career and employment-related courses Yes 221 (44.9) 64 (54.3) 158 (42.0) 5.41 (.025) No 271 (55.1) 53 (45.7) 218 (58.0) Nursing college satisfaction Education supporting facilities 3.3 ± 0.9 3.2 ± 1.0 3.4 ± 0.9 2.10 (.036) Career related supporting system 3.2 ± 0.9 3.0 ± 0.9 3.2 ± 0.9 2.12 (.035) Overall satisfaction 3.2 ± 0.9 3.1 ± 1.1 3.3 ± 0.9 2.30 (.022) Student loan Yes 150 (30.5) 46 (39.7) 104 (27.7) 6.02 (.016) No 342 (69.5) 70 (60.3) 272 (72.3) Physical and mental health factor Subjective health status 3.5 ± 0.9 3.3 ± 1.0 3.6 ± 0.9 3.61 (< .001) Satisfied with my life on a personal level 5.0 ± 1.4 4.5 ± 1.6 5.1 ± 1.2 3.76 (< .001) Satisfied with my life in the relational aspect 5.2 ± 1.3 4.8 ± 1.5 5.3 ± 1.2 2.73 (.007) Satisfied with the group I belong to 4.8 ± 1.4 4.2 ± 1.7 5.0 ± 1.3 4.78 (< .001) Emotions during the past month Joyful 4.9 ± 1.3 4.5 ± 1.5 5.0 ± 1.2 3.05 (.003) Happy 4.8 ± 1.4 4.5 ± 1.5 4.9 ± 1.3 2.75 (.007) Comfortable 4.7 ± 1.4 4.3 ± 1.6 4.8 ± 1.3 2.71 (.007) Annoyed 4.1 ± 1.5 4.6 ± 1.4 4.0 ± 1.5 -3.73 (< .001) Negative 3.8 ± 1.6 4.3 ± 1.5 3.6 ± 1.6 -4.45 (< .001) Listless 3.6 ± 1.8 4.2 ± 1.8 3.5 ± 1.7 -3.89 (< .001) * M = Mean; SD = Standard Deviation Decision Tree Model for NGNs’ Turnover Intention The CHAID model yielded a predictive accuracy of 78.9%. The model's performance was analyzed using a ROC curve, as shown in Fig. 2 . The AUC of the model for predicting turnover intention was 0.80, indicating good discriminative performance. Figure 3 displays the relative importance of the study variables that predict turnover intention among NGNs. The most influential predictors, in descending order of importance, were: satisfaction with the promotion system, satisfaction with social reputation for one’s job, receipt of incentive payments, feelings of listlessness experienced during the past month, participation in career and employment-related courses during college, permanent employment status, and satisfaction with salary or income. Figure 4 depicts the final decision tree model for predicting turnover intention. The model generated five layers and a total of 11 nodes. Among the eight terminal nodes, three were identified as high-risk groups with higher turnover intention, while five indicated a higher likelihood of retention. The blue-boxed nodes in Fig. 4 represent the group with the highest level of turnover intention. One hundred percent of nurses in node 8—those who reported low satisfaction (≤ 2) with opportunities for personal growth in their current jobs, had moderate or lower satisfaction (≤ 3) with the social reputation of their jobs, and did not receive or were unaware of incentive payments—expressed an intention to leave their current workplace. In contrast, the red-boxed nodes in Fig. 4 represent the group with the highest rate of retention intention. Approximately 97% of nurses in node 15—those who reported at least moderate to high satisfaction (> 2) with opportunities for personal growth in their current jobs, were not employed part-time, expressed at least moderate to high satisfaction (> 2) with the promotion system, experienced feelings of listlessness less frequently over the past month, and held permanent positions—intended to retain their jobs. Discussion This study identified factors associated with the turnover intention of NGNs with less than one year of experience in the current workplace using a decision tree analysis method. The findings revealed that 23.6% (N = 116) of participants expressed turnover intention. Although the measurement methods and timing for assessing turnover intention varied across studies, the prevalence observed in this study falls within the range reported in previous research (21–35.2%) [ 10 , 13 ]. A considerable number of nurses who expressed turnover intention ultimately left their jobs within a short period [ 8 ]. Early turnover of NGNs results not only in low self-efficacy for individuals but also imposes a significant financial burden on administration due to frequent recruitment process and repetitive training [ 23 , 24 ]. This study found that the most significant predictors of turnover intention among NGNs were lower levels of job satisfaction related to personal development. Additionally, lower satisfaction with the social reputation of the job and incentive payments were also highly ranked predictors. These results reveal slightly different findings compared to previous literature using the same dataset from the 2010 GOMS [ 25 ]. Their study identified job status as the strongest predictor of turnover among NGNs, followed by monthly wages, the level of job satisfaction, and the number of hospitals in the region. This discrepancy may be attributed to changes in job-related values among new nurses over time. The participants in this study were part of Generation Y and Z, born between 1981 and 2012. Compared to baby boomer nurses and Generation X, the Y and Z generations seek opportunities for personal and professional growth [ 26 ]. As a result, they prefer challenges at work, and if they feel unmotivated, they are more likely to leave their positions [ 27 ]. More recently, Lee and Ji [ 28 ] reported that professional nursing values among Generation Z nurses (born after 1995) scored the lowest in their first year of employment, although these scores increased slightly afterward. Professionalism, knowledge, and good communication skills were highly ranked professional nursing values, whereas the value of dedication ranked the lowest. Additionally, findings from a study by Oh and Oh [ 12 ] align with this study's results. NGNs during the COVID-19 pandemic tended to show greater turnover intention when they were male, married, and experienced lower job satisfaction, along with negative perceptions about human resource management practices, including employee compensation management, training, and career development. Considering these findings, nurse leaders need to develop strategies that provide opportunities for meaningful achievements, such as personal development and promotion, in order to motivate the new generation of nurses to remain in their workplaces for a longer duration. Unlike previous studies, this research uncovers the significant relationship between job satisfaction related to the social reputation of a job and turnover intention. While there are limited studies addressing the role of social reputation in understanding turnover dynamics, Wang and colleagues [ 29 ] reported that intrinsic factors of job motivation, such as personal growth and social recognition, were significantly associated with the turnover intention of nurses working in nursing homes. Organizational reputation is an assessment by stakeholders regarding an organization's ability to meet their expectations [ 30 ]. It is also a resource that can create competitive advantages for the organization, and previous literature has found a significant negative relationship between organizational reputation and employees’ turnover intention [ 30 ]. These findings suggest that efforts by institutions to improve their social reputation should be integrated into strategies aimed at preventing early turnover among NGNs. Monetary rewards, such as monthly wages and incentives, are well-known factors influencing both turnover intention and actual turnover in previous literature [ 12 ]. When coupled with perceptions of heavy workload and stress during the adaptation period in the early stages of their careers, lower levels of wages or incentives may be perceived as insufficient rewards. Kim and Lee [ 25 ] also reported that low monthly wages were the most significant factor contributing to turnover intention among newly graduated nurses. Therefore, employers should make continuous efforts to establish a realistic and systematic monetary compensation system that considers levels of competency as well as work experience. The group with the intention to stay demonstrated higher levels of job satisfaction related to opportunities for personal growth and tended to have full-time job status. Full-time and part-time job status is linked to job security, which has been repeatedly reported as a predictor of turnover intention [ 31 ]. Job security encompasses expectations regarding career advancement opportunities over time [ 31 ]. A lack of job security is associated with lower job satisfaction, weaker organizational commitment, poorer self-reported health and well-being, lower productivity, and higher turnover intention [ 32 , 33 ]. Additionally, a positive relationship was found between full-time/part-time status and the level of job satisfaction with the promotion system in the workplace. Specifically, full-time participants were more likely to remain employed when they were satisfied with the promotion system. Given the importance of promotion potential and professional development in decision-making about whether to stay or leave a job, offering more full-time positions for NGNs are likely contribute to longer retention. Those with higher job satisfaction related to the promotion system were more likely to remain employed when they experienced lower levels of listlessness. This finding highlights that mental health plays a crucial role in job retention, even in the presence of professional achievements such as promotions. Numerous studies have found negative correlations between mental health status and turnover intention among nurses. Specifically, nurses with turnover intention reported significantly higher frequencies of depressive moods [ 8 ]. NGNs often experience psychological consequences including depression, anxiety, and burnout due to insufficient training and excessive workloads [ 6 , 34 ]. These findings indicate that administrative support for NGNs should prioritize mental health management alongside fostering professional competency and maintaining an adequate workload. The experience of taking job-preparation classes during the undergraduate program emerged as an important predictor of turnover intention (Figs. 3 and 4 ). This finding is in line with previous studies suggesting that undergraduate nursing programs that effectively prepare students for clinical practice positively influence retention [ 35 ]. A scoping review emphasized the essential values that undergraduate nursing programs need to cultivate to promote retention after qualification, including resilience, commitment, preparation for transition, and perceived knowledge and confidence regarding nursing skills [ 35 ]. According to Kenny et al. [ 36 ], NGNs who were satisfied with their undergraduate education in work preparation reported significantly higher job satisfaction. These findings underline the importance of undergraduate nursing programs in preparing students psychologically and intellectually for the transition into practice, which can enhance job satisfaction and retention during the early stages of their nursing careers. Limitations Despite the significance of this study, there are a couple of limitations. First, due to the nature of cross-sectional data, the actual number of NGNs who left their jobs is not known, making it impossible to draw causal inferences. Longitudinal studies could be instrumental in identifying the causal relationships among variables and characteristics of NGNs who leave their workplaces in a short period of employment. Second, since this study was conducted in South Korea, caution is needed when generalizing the findings to other countries. However, the findings of this study highlight important reasons why current younger-generation nurses plan to leave their current workplaces based on a predictive model that considers various interactions among variables. Conclusion This study identified predictors of turnover intention among NGNs with less than one year of experience in the current workplaces by analyzing a large national dataset using a decision tree model. The findings of this study contribute to the development of effective human resource management strategies in the work environment for nurses, as well as educational initiatives in undergraduate nursing programs aimed at enhancing the retention of NGNs. To reduce turnover intention and prevent early turnover among NGNs, employers should create and provide various opportunities for promotion and personal development, along with monetary rewards and emotional support. Additionally, nurse educators should encourage students to engage actively in coursework that addresses professional nursing values and the organizational expectations they will encounter in the workplace. Declarations Ethics approval and consent to participate This study was exempted from approval by the Institutional Review Board of Kyungpook National University in accordance with the Declaration of Helsinki (No. 2024-0009). Human ethics and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are available from the Korea Employment Information Service (Graduates Occupational Mobility Survey 2016- 2020) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Competing interests There is no competing interests to declare. Funding There was no financial assistance for conducting this study. Authors' contributions Conceptualization; MM, Data curation; MM, HK, Formal analysis; MM, Investigation; MM, HK, Methodology; MM, Project administration; MM, Resources; MM, Supervision; MM, Validation; MM, HK, Visualization; MM, HK, Roles/Writing - original draft; MM, HK, Writing - review & editing: MM, HK. Acknowledgements Not applicable. References Ministry of Health and Welfare. OECD Health Statistics 2024 [Internet]. 2024 [cited 2024 Jul 7]. Available from: https://www.mohw.go.kr/board.es?mid=a10411010100&bid=0019&act=view&list_no=1483195 NSI Nursing Solutions, Inc. 2021 NSI national health care retention & RN staffing report [Internet]. 2021 [cited 2024 Jul 7]. Available from: https://www.nsinursingsolutions.com/Documents/Library/NSI_National_Health_Care_Retention_Report.pdf Dunton N, Gajewski B, Klaus S, Pierson B. The relationship of nursing workforce characteristics to patient outcomes. 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Predicting new graduate nurses’ retention during transition using decision tree methods: A longitudinal study. J Nurs Manag. 2024;2024:4687000. https://doi.org/10.1155/2024/4687000 Reebals C, Wood T, Markaki A. Transition to practice for new nurse graduates: Barriers and mitigating strategies. West J Nurs Res. 2022;44(4):416-29. https://doi.org/10.1177/0193945921997925 Lee Y, Kang J. Related factors of turnover intention among Korean hospital nurses: A systematic review and meta-analysis. Korean J Adult Nurs. 2018;30(1):1-17. https://doi-org.libproxy.knu.ac.kr/10.7475/kjan.2018.30.1.1 Obermeyer Z, Emanuel EJ. Predicting the future - Big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216-9. https://doi.org/10.1056/NEJMp1606181 O’Brien RL, O’Brien MW. CE: Nursing orientation to data science and machine learning. Am J Nurs. 2021;121(4):32-9. https://doi.org/10.1097/01.NAJ.0000737302.08232.0d Podgorelec V, Kokol P, Stiglic B, Rozman I. Decision trees: An overview and their use in medicine. J Med Syst. 2002;26(5):445-63. https://doi.org/10.1023/A:1016409317640 Kass GV. An exploratory technique for investigating large quantities of categorical data. J R Stat Soc Ser C Appl Stat. 1980;29(2):119-27. Korea Employment Information Service. Introduction to GOMS [Internet]. 2022 [cited 2024 Jul 7]. Available from: https://www.keis.or.kr/keis/ko/conts/140/web.do Yeo B, Grant D. Predicting service industry performance using decision tree analysis. Int J Inf Manag. 2018;38(1):288-300. https://doi.org/10.1016/j.ijinfomgt.2017.10.002 Kwon JY, Karim ME, Topaz M, Currie LM. Nurses "Seeing forest for the trees" in the age of machine learning: Using nursing knowledge to improve relevance and performance. Comput Inform Nurs. 2019;37(4):203-12. https://doi.org/10.1097/CIN.0000000000000508 Bae SH, Cho M, Kim O, Pang Y, Cha C, Jung H, et al. Predictors of actual turnover among nurses working in Korean hospitals: A nationwide longitudinal survey study. J Nurs Manag. 2021;29(7):2102-14. https://doi.org/10.1111/jonm.13347 Zhang J, Xia L, Wang Y, Yi T, Wang Y, Park E, et al. Predictive factors of turnover intention of newly graduated nurses in their first year of employment: a longitudinal study. BMC Nurs. 2024;23(1):522. https://doi.org/10.1186/s12912-024-02205-3 Kim S, Lee K. Predictors of turnover among new nurses using multilevel survival analysis. J Korean Acad Nurs. 2016;46(5):733-43. https://doi.org/10.4040/jkan.2016.46.5.733 Anselmo-Witzel S, Orshan SA, Heitner KL, Bachand J. Are Generation Y nurses satisfied on the job? Understanding their lived experiences. J Nurs Adm. 2017;47(4):232-7. https://doi.org/10.1097/NNA.0000000000000470 Christopher SA, Chiarella EM, Waters D. Can Generation Y nurses supply areas of shortage? New graduate challenges in today’s job market. Aust J Adv Nurs. 2015;33:35-44. https://doi.org/10.37464/2016.332.1549 Lee T, Ji Y. Professional nursing values in nursing students during transitional period to nurses from the perspective of generation Z: A longitudinal study. J Adv Nurs. 2024;2024:jan.16637. https://doi.org/10.1111/jan.16637 Wang E, Hu H, Mao S, Liu H. Intrinsic motivation and turnover intention among geriatric nurses employed in nursing homes: roles of job burnout and pay satisfaction. Contemp Nurse. 2019;55(2-3):195-210. https://doi.org/10.1080/10376178.2019.1641120 Beheshtifar M, Allahyary MH. Study the relationship among organizational reputation with organizational commitment and employees’ turnover intention. Int Res J Appl Basic Sci. 2013;6(10):1467-78. Sokhanvar M, Kakemam E, Chegini Z, Sarbakhsh P. Hospital nurses' job security and turnover intention and factors contributing to their turnover intention: A cross-sectional study. Nurs Midwifery Stud. 2018;7(3):133-40. https://doi.org/10.4103/nms.nms_2_17 Emberland J, Rundmo T. Implications of job insecurity perceptions and job insecurity responses for psychological well-being, turnover intentions and reported risk behavior. Saf Sci. 2010;48:452-9. https://doi.org/10.1016/j.ssci.2009.12.002 Laine M, van der Heijden BI, Wickström G, Hasselhorn HM, Tackenberg P. Job insecurity and intent to leave the nursing profession in Europe. Int J Hum Resour Manag. 2009;20(2):420-38. https://doi.org/10.1080/09585190802673486 Bakker EJ, Kox JH, Boot CR, Francke AL, van der Beek AJ, Roelofs PD. Improving mental health of student and novice nurses to prevent dropout: A systematic review. J Adv Nurs. 2020;76(10):2494-509. https://doi.org/10.1111/jan.14453 Collard SS, Scammell J, Tee S. Closing the gap on nurse retention: A scoping review of implications for undergraduate education. Nurse Educ Today. 2020;84:104253. https://doi.org/10.1016/j.nedt.2019.104253 Kenny P, Reeve R, Hall J. Satisfaction with nursing education, job satisfaction, and work intentions of new graduate nurses. Nurse Educ Today. 2016;36:230-5. https://doi.org/10.1016/j.nedt.2015.10.023 Additional Declarations No competing interests reported. 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Kang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBACAyBmbAAS/DxIomARgloke0jWYnCGWC3mEjlmD2fU3LHbfObws4df22zyGNgPP2CcuQe3FssZOeaGG449S952ts3cWLYtrZiBJ82AccMzPA67kWMm+YDtcLLZeQYzaclthxMbGHIYGB8cIKTl3+Fk4372bxAt/G+I0LKx7bCdAW+PmeRHkBYJoC0b8Gk586xMcmbf4QSJM2fKpBn/pSW2STwzODgDn5bjydske74dtufvSd8m+eOMTWI/f/LDhz14tDAIJIApoK8ZGJhBKYANiPFpACYUiLQ9iGD8gVfpKBgFo2AUjFQAAPBzWl8l28rjAAAAAElFTkSuQmCC","orcid":"","institution":"Kangwon National University","correspondingAuthor":true,"prefix":"","firstName":"Hyunwook","middleName":"","lastName":"Kang","suffix":""}],"badges":[],"createdAt":"2025-07-09 09:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7082239/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7082239/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12912-025-04041-5","type":"published","date":"2025-11-18T15:59:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87361277,"identity":"e3782cd4-bac3-43fc-86a2-df3012fe3364","added_by":"auto","created_at":"2025-07-23 05:52:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":30862,"visible":true,"origin":"","legend":"\u003cp\u003eFlow Diagram of Participant Selection\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7082239/v1/cad7b9e73d714bbea298f869.png"},{"id":87361278,"identity":"04ec722e-9e08-4cc8-90ef-9184e68383ea","added_by":"auto","created_at":"2025-07-23 05:52:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57695,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curve for the Decision Tree\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7082239/v1/2e083b940dae203989b12589.png"},{"id":87361281,"identity":"7afef884-217d-4d64-9dea-852c9ff38f2a","added_by":"auto","created_at":"2025-07-23 05:52:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38151,"visible":true,"origin":"","legend":"\u003cp\u003eRelative Importance of Predictors of Newly Graduated Nurses’ Turnover Intention\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7082239/v1/a42dafcdd085d02ad6fb8c18.png"},{"id":87361283,"identity":"35e47722-04e8-4a69-9aed-4be0dd2eef95","added_by":"auto","created_at":"2025-07-23 05:52:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":106915,"visible":true,"origin":"","legend":"\u003cp\u003eDecision Tree for Predictors of Newly Graduated Nurses’ Turnover Intention\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7082239/v1/87ce264bee20d77d6c7131e0.png"},{"id":96650410,"identity":"dde5ad9c-89bb-4f6c-b4aa-ed57cffd3eb8","added_by":"auto","created_at":"2025-11-24 16:12:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1032305,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7082239/v1/9d77a7f2-d7bc-4579-ba1c-c9a1f03d7a98.pdf"},{"id":87363044,"identity":"fb74d48a-7b8a-4a45-afaa-7367b0c0b15e","added_by":"auto","created_at":"2025-07-23 06:00:18","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":31299,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary10709.docx","url":"https://assets-eu.researchsquare.com/files/rs-7082239/v1/bf29e024a54e4e04103624b0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Turnover Intention Among Newly Graduated Nurses in South Korea: A Decision Tree Analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eThe adequate nurse-patient ratio is essential for maintaining quality nursing care and maximizing patient outcomes. However, the global nursing shortage has become a significant concern. South Korea is notorious for its low nurse-patient ratio which is likely attributed to high turnover rates. As of the year 2000, the number of licensed nurses working in clinical settings in South Korea is significantly smaller than that in the Organization for Economic Co-operation and Development (OECD) countries (4.4 vs. 8.0 per 1,000 population), while the number of new nursing graduates is greater than in OECD countries (42.4 vs. 31.4 per 100,000 population) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The turnover rate of Korean nurses is approximately three times higher than that of nurses in the United States as of 2016 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These results strongly indicate nursing graduates in South Korea are likely to remain in their positions only for a brief duration. High turnover rates among nurses have a considerable negative impact on patient outcomes, including lower activities of daily living, worsening existing pressure ulcers or developing new ones, prolonged hospitalization, and increased medical errors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Consequently, nurses’ high turnover contributes to increasing healthcare costs [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIt was reported that the turnover rate of newly graduated nurses (NGNs) is higher than that those with more than five years of working experience [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. NGNs are often experience a substantial level of job-related stress during their short transition period from students to competent nurses, which lasts for a couple of years after graduation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. They are required to adapt to their new roles as professional healthcare personnel, learn clinical knowledge and build relationships with various types of colleagues. Dissatisfaction with job experiences and difficulty in adapting to their new roles may contribute to high turnover rates. Lee [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] reported that 25% of nurses left their job within one year after graduation.\u003c/p\u003e\u003cp\u003eTurnover intention is defined as “the thoughts and behaviors among organizational members to voluntarily leave the organization” [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and it is a strong predictor of actual turnover among nurses [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. One study reported that approximately 20% of nurses expressing turnover intention ended up leaving their positions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Turnover intention is considered a complex concept that involves economic, psychological, and organizational outcomes, all of which depend on the interaction of various factors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, numerous studies have identified correlates of turnover intention among NGNs using traditional statistical methods. Factors related to NGNs’ turnover intention include work schedules, the desirability of the hospital, opportunities for professional development, becoming a part of a team and availability of practical support [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Individual factors include younger age, male gender, higher levels of education, marital status, and lower work experience [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A more recent study reported that NGNs’ turnover intention was 12.8% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This study identified significant associations between turnover intention and factors such as higher job stress, sleep disturbances, poor subjective health status, and working in intensive care units. Job satisfaction has consistently been identified as a strong predictor of both turnover intention and actual turnover [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAmong NGNs, the initial job experience is often described as a period of transition or adaptation. Studies on NGNs’ turnover have reported that transitional shock, which is characterized by the gap between learned theory and practical application, overwhelming workloads, lack of social support, and conflicts with coworkers, significantly contributes to turnover intention [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These findings suggest that a wide range of factors contribute to the turnover intention of NGNs. Therefore, accurately understanding this phenomenon is crucial for maximizing organizational efficacy and enhancing the quality of nursing care, which is directly linked to positive patient outcomes.\u003c/p\u003e\u003cp\u003eHowever, most of these studies have limitations due to small sample sizes and geographical restrictions. In particular, since most studies have verified only a limited number of variables selected by researchers, it is difficult to comprehensively identify the influencing factors as a whole [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Accurately predicting turnover intention using large datasets that incorporate multifaceted factors is crucial for developing more effective strategies to increase retention rates of NGNs.\u003c/p\u003e\u003cp\u003eAnalyzing large datasets using the machine learning methods can more accurately predict influencing factors by considering the interactions and nonlinear relationships among various independent variables [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In particular, decision tree models, which are a type of machine learning algorithm, provide a valuable analytical framework for predicting related factors by identifying meaningful patterns and rules. These models have demonstrated effectiveness in predicting relevant factors to support decision-making in healthcare settings, and often outperformed traditional methods in specific contexts [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Specifically, the Chi-square Automatic Interaction Detection (CHAID) decision trees employ the chi-square test of association to assess the goodness of fit between observed and expected outcomes, thereby determining splitting rules and node formation. The CHAID decision tree has a unique tree structure that can generate non-binary trees, which means some splits may have more than two branches, making it particularly suitable for analyzing larger datasets [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe purpose of this study is to identify factors influencing on turnover intention among NGNs with less than one year of experience in the current workplaces in South Korea, using a decision tree model based on the data from the 2016–2020 Graduate Occupational Mobility Survey (GOMS).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design and Data Source\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study conducted a secondary analysis using the recent five years (2016 to 2020) of data from the GOMS, produced by the Korea Employment Information Service (KEIS). The GOMS is a nationally representative survey of newly graduated college students in Korea [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The GOMS sample comprises approximately 4% of all annual number of college graduates in Korea, with sampling allocation based on academic majors. The survey is conducted annually between September and November of each year following graduation (18 to 24 months after college completion). GOMS includes demographic characteristics and a wide range of job-related variables to analyze the job-related mobility of college graduates in Korea.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Sample\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFrom the GOMS data from 2016 to 2020, 1,555 nursing graduates were identified. Among these, 205 NGNs who were not working at the time of the survey were excluded. Based on the purpose of this study, the final study sample comprised 492 NGNs with less than one year of experience at their current workplaces (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasures\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutcome Variable\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess turnover intentions among NGNs, we used a GOMS item that asked whether participants were actively preparing to change jobs. This variable was measured using a binary scale.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePredictive Variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo examine the factors influencing turnover intentions among NGNs, a total of 110 variables were categorized into four groups: personal factors, current job-related factors, colleged-related factors, and physical-mental health related factors. The personal factors included six demographic characteristics: age, gender, marital status, living arrangement, average monthly parental income, and parents’ current assets. The current job-related factors comprised 42 items, including part-time status, modes of transportation to the workplace, commuting time, shift work status, benefits provided by the current workplace, permanent employment status, internship experience, regular working days, regular working hours and overtime hours per week, average number of working days on holidays, and average monthly income, as well as 15 subdomains of job satisfaction rated from 1 (very dissatisfied) to 5 (very satisfied).\u003c/p\u003e\u003cp\u003eThe college-related factors included 27 items related to reasons for selecting the college and major, participation in career preparation programs, satisfaction with various aspects of the college experience, grade point average, items related to student loans, and career plans prior to graduation. Finally, the physical and mental health-related factors comprised 20 items, including current subjective health status (1 = not very heathl ~ 5 = very healthy), current health behaviors, physical limitations, items on life satisfaction (1 = not at all ~ 7 = very much so), and items assessing emotional experiences over the past month rated from 1 (did not feel at all) to 7 (always felt it).\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eMeans, standard deviations, and frequency were calculated to describe characteristics of participants using the IBM SPSS statistics for Windows version 29. To reduce the risk of overfitting and enhance the predictive performance of the model, univariate analyses using chi-square tests and independent t-tests were conducted. As a result, a total of 45 significant variables were identified and included in the final decision tree model.\u003c/p\u003e\u003cp\u003eDecision tree analysis with the CHAID algorithm was conducted to identify factors influencing turnover intention among NGN’s using IBM SPSS modeler 17.0. The dataset was randomly divided into a training set (70%) and a testing set (30%), which is commonly used ratio for predictive modeling. For missing values, decision trees categorized them as separate branches in each split [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The relative importance of each predictor was determined on a scale from 0 to 1. The values are derived from sensitivity measures of the predictors, reflecting the amount of variance in the target variable that is explained when the value of a specific predictor is known. A higher score indicates greater importance of the predictor [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Model performance was evaluated using accuracy and the area under the receiver operating characteristic (ROC) curve (AUC). An AUC value between 0.70 and 0.80 is interpreted as indicating moderate discriminative ability, while a value above 0.80 is considered indicative of strong discriminative performance [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eCharacteristics of Participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe demographic characteristics of the participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of the participants was 25.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8 years and 86.0% were women. The majority of them (96.1%) was single. Of the total participants, 116 individuals (23.6%) reported an intention to leave their current job positions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic characteristics of participants (N\u0026thinsp;=\u0026thinsp;492)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%) or (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003csup\u003e*\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e423 (86.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69 (14.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e473 (96.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (3.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving with parents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e241 (49.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e251 (51.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage monthly income (million won)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e248.6\u0026thinsp;\u0026plusmn;\u0026thinsp;65.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubjective health status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003eM\u0026thinsp;=\u0026thinsp;Mean; SD\u0026thinsp;=\u0026thinsp;Standard Deviation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDescriptive characteristics of predictors are shown in Supplementary 1. Regarding current job-related factors, 93.1% were employed full-time, and 62.8% were engaged in shift work. Among employee benefits, legal severance pay (89.4%) and incentive payments (82.7%) were the most commonly provided, while paid weekly holidays (60.6%) was the least provided. The majority (79.1%) were permanent employees, and the average monthly wage was 2.49\u0026nbsp;million won. In terms of satisfaction in the current job, the participants were the most satisfied with employment security (3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9) and were the least satisfied with salary or income (3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0). Satisfaction with social reputation for their jobs and the promotion system was reported as 3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 and 3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9, respectively, on a 5-point Likert scale.\u003c/p\u003e\u003cp\u003eRegarding nursing college factors, the primary reason for selecting a nursing major was promising job and employment prospects (62.0%). Among career selection and job preparation activities, 52.4% participated in aptitude tests or other career psychometric assessments, and 44.9% attended courses related to career and job preparation. Participants reported the highest satisfaction with the curriculum and educational content in their nursing schools (3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9) and the lowest satisfaction with educational support facilities.\u003c/p\u003e\u003cp\u003eRegarding physical and mental health factors, the average score for subjective health status was 3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 on a 5-point scale. Among emotional experiences reported during the past month, \u0026ldquo;joyful\u0026rdquo; was the most frequently experienced emotion (4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3), while \u0026ldquo;listless\u0026rdquo; was the least frequently experienced (3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8).\u003c/p\u003e\u003cp\u003e\u003cb\u003eUnivariate Relationships Between Predictors and NGNs\u0026rsquo; Turnover Intention\u003c/b\u003e\u003c/p\u003e\u003cp\u003e30 current job-related factors, five college-related factors, and ten physical and mental health-related factors showed statistically significant associations with nurses\u0026rsquo; turnover intention (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTable 2. Univariate relationships between predictors and newly graduated nurses\u0026rsquo; turnover intention (N=492)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"105%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003en (%) or\u003c/p\u003e\n \u003cp\u003eM \u0026plusmn; SD\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eTurnover intention\u003c/p\u003e\n \u003cp\u003en (%) or M \u0026plusmn; SD\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eor t (\u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n = 116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n = 376)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent job-related factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003ePart-time employment status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e34(6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e20(17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e14(3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e25.18 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e458 (93.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e96 (82.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e362 (96.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eShift work status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e309 (62.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e62 (53.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e247 (65.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e6.03 (.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e181 (26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e54 (46.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e127 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eProvided benefit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eLegal severance pay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e440 (93.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e93 (87.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e347 (95.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e8.73 (.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e29 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e13 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e16 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003ePaid vacation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e401 (88.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e84 (82.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e317 (90.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e4.55 (.033)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e53 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e18 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e35 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eOvertime pay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e376 (81.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e77 (73.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e299 (83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e5.24 (.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e88 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e28 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e60 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eIncentive payment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e407 (85.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e83 (74.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e324 (89.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e14.69 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e67 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e28 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e39 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003ePaid weekly holiday\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e298 (78.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e51 (65.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e247 (81.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e9.48 (.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e83 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e27 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e56 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003ePermanent employment status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e389 (79.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e76 (65.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e313 (83.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e17.87 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e101 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e40 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e61 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eAverage monthly income (million won)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e248.6 \u0026plusmn; 65.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e227.0 \u0026plusmn; 74.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e255.2 \u0026plusmn; 61.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.69 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eJob satisfaction with\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 14.1509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2642%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eSalary or income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"15\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.1 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.7 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.3 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e5.08 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eEmployment security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.8 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.9 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.86 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eWorking environment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.6 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.4 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.7 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e2.71 (.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eWorking time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.3 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.1 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.4 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e2.27 (.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003ePersonal growth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.4 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.9 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e6.03 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eInterpersonal relationship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.3 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.6 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.03 (.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eEmployee benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.4 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.0 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e5.11 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003ePromotion system\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.2 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.7 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.3 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e6.52 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eSocial reputation for one\u0026rsquo;s job\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.6 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.1 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.7 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e6.46 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eAutonomy and authority\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.4 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.0 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e4.23 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eSocial reputation for workplace \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.6 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.3 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.7 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e4.33 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMatch between job and personal interests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.1 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.6 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e5.84 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eJob-related education or training\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.0 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.6 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e6.89 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eOverall for current job\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.4 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.0 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e6.41 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eOverall for main duties\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.4 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.1 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e4.51 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eJob-educational level match\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.1 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.8 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.1 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.19 (.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eJob-skill level match\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.1 \u0026plusmn; 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2.9 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.1 \u0026plusmn; 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e2.29 (.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eJob-academic major match\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.9 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e4.1 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e4.61 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eEnrollment status in the current workplace \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eHealth insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e456 (92.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e354 (94.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e102 (89.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e6.66 (.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e17 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e5 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e12 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eUnaware\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e19 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e9 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e10 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eEmployment insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e432 (87.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e94 (81.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e338 (89.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e9.07 (.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e29 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e8 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e21 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eUnaware\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e31 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e14 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e17 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eWorker\u0026rsquo;s compensation insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e407 (82.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e87 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e320 (85.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e8.35 (.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e25 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e6 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e19 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eUnaware\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e60 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e23 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e37 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCollege factor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eParticipation in career selection and job preparation program \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eCareer and employment-related courses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e221 (44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e64 (54.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e158 (42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e5.41 (.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e271 (55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e53 (45.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e218 (58.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eNursing college satisfaction\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eEducation supporting facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.3 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.2 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.4 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e2.10 (.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eCareer related supporting system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.2 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.0 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.2 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e2.12 (.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eOverall satisfaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.2 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.1 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.3 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e2.30 (.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eStudent loan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e150 (30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e46 (39.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e104 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e6.02 (.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e342 (69.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e70 (60.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e272 (72.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical and mental health factor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eSubjective health status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.3 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.6 \u0026plusmn; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.61 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eSatisfied with my life on a personal level\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"3\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e5.0 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.5 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e5.1 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.76 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eSatisfied with my life in the relational aspect\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e5.2 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.8 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e5.3 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e2.73 (.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eSatisfied with the group I belong to\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.8 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.2 \u0026plusmn; 1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e5.0 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e4.78 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eEmotions during the past month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.1509%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2642%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eJoyful\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"6\" valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.9 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.5 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e5.0 \u0026plusmn; 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e3.05 (.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eHappy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.8 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.5 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e4.9 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e2.75 (.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eComfortable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.7 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.3 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e4.8 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e2.71 (.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eAnnoyed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.1 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.6 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e4.0 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-3.73 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.8 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.3 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.6 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-4.45 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eListless\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3.6 \u0026plusmn; 1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e4.2 \u0026plusmn; 1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.5 \u0026plusmn; 1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e-3.89 (\u0026lt; .001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003eM = Mean; SD = Standard Deviation\u003c/p\u003e\u003cp\u003e\u003cb\u003eDecision Tree Model for NGNs\u0026rsquo; Turnover Intention\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe CHAID model yielded a predictive accuracy of 78.9%. The model's performance was analyzed using a ROC curve, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The AUC of the model for predicting turnover intention was 0.80, indicating good discriminative performance. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the relative importance of the study variables that predict turnover intention among NGNs. The most influential predictors, in descending order of importance, were: satisfaction with the promotion system, satisfaction with social reputation for one\u0026rsquo;s job, receipt of incentive payments, feelings of listlessness experienced during the past month, participation in career and employment-related courses during college, permanent employment status, and satisfaction with salary or income.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e depicts the final decision tree model for predicting turnover intention. The model generated five layers and a total of 11 nodes. Among the eight terminal nodes, three were identified as high-risk groups with higher turnover intention, while five indicated a higher likelihood of retention.\u003c/p\u003e\u003cp\u003eThe blue-boxed nodes in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e represent the group with the highest level of turnover intention. One hundred percent of nurses in node 8\u0026mdash;those who reported low satisfaction (\u0026le;\u0026thinsp;2) with opportunities for personal growth in their current jobs, had moderate or lower satisfaction (\u0026le;\u0026thinsp;3) with the social reputation of their jobs, and did not receive or were unaware of incentive payments\u0026mdash;expressed an intention to leave their current workplace.\u003c/p\u003e\u003cp\u003eIn contrast, the red-boxed nodes in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e represent the group with the highest rate of retention intention. Approximately 97% of nurses in node 15\u0026mdash;those who reported at least moderate to high satisfaction (\u0026gt;\u0026thinsp;2) with opportunities for personal growth in their current jobs, were not employed part-time, expressed at least moderate to high satisfaction (\u0026gt;\u0026thinsp;2) with the promotion system, experienced feelings of listlessness less frequently over the past month, and held permanent positions\u0026mdash;intended to retain their jobs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study identified factors associated with the turnover intention of NGNs with less than one year of experience in the current workplace using a decision tree analysis method. The findings revealed that 23.6% (N\u0026thinsp;=\u0026thinsp;116) of participants expressed turnover intention. Although the measurement methods and timing for assessing turnover intention varied across studies, the prevalence observed in this study falls within the range reported in previous research (21\u0026ndash;35.2%) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A considerable number of nurses who expressed turnover intention ultimately left their jobs within a short period [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Early turnover of NGNs results not only in low self-efficacy for individuals but also imposes a significant financial burden on administration due to frequent recruitment process and repetitive training [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study found that the most significant predictors of turnover intention among NGNs were lower levels of job satisfaction related to personal development. Additionally, lower satisfaction with the social reputation of the job and incentive payments were also highly ranked predictors. These results reveal slightly different findings compared to previous literature using the same dataset from the 2010 GOMS [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Their study identified job status as the strongest predictor of turnover among NGNs, followed by monthly wages, the level of job satisfaction, and the number of hospitals in the region. This discrepancy may be attributed to changes in job-related values among new nurses over time.\u003c/p\u003e\u003cp\u003eThe participants in this study were part of Generation Y and Z, born between 1981 and 2012. Compared to baby boomer nurses and Generation X, the Y and Z generations seek opportunities for personal and professional growth [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. As a result, they prefer challenges at work, and if they feel unmotivated, they are more likely to leave their positions [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. More recently, Lee and Ji [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] reported that professional nursing values among Generation Z nurses (born after 1995) scored the lowest in their first year of employment, although these scores increased slightly afterward. Professionalism, knowledge, and good communication skills were highly ranked professional nursing values, whereas the value of dedication ranked the lowest. Additionally, findings from a study by Oh and Oh [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] align with this study's results. NGNs during the COVID-19 pandemic tended to show greater turnover intention when they were male, married, and experienced lower job satisfaction, along with negative perceptions about human resource management practices, including employee compensation management, training, and career development. Considering these findings, nurse leaders need to develop strategies that provide opportunities for meaningful achievements, such as personal development and promotion, in order to motivate the new generation of nurses to remain in their workplaces for a longer duration.\u003c/p\u003e\u003cp\u003eUnlike previous studies, this research uncovers the significant relationship between job satisfaction related to the social reputation of a job and turnover intention. While there are limited studies addressing the role of social reputation in understanding turnover dynamics, Wang and colleagues [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] reported that intrinsic factors of job motivation, such as personal growth and social recognition, were significantly associated with the turnover intention of nurses working in nursing homes. Organizational reputation is an assessment by stakeholders regarding an organization's ability to meet their expectations [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. It is also a resource that can create competitive advantages for the organization, and previous literature has found a significant negative relationship between organizational reputation and employees\u0026rsquo; turnover intention [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These findings suggest that efforts by institutions to improve their social reputation should be integrated into strategies aimed at preventing early turnover among NGNs.\u003c/p\u003e\u003cp\u003eMonetary rewards, such as monthly wages and incentives, are well-known factors influencing both turnover intention and actual turnover in previous literature [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. When coupled with perceptions of heavy workload and stress during the adaptation period in the early stages of their careers, lower levels of wages or incentives may be perceived as insufficient rewards. Kim and Lee [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] also reported that low monthly wages were the most significant factor contributing to turnover intention among newly graduated nurses. Therefore, employers should make continuous efforts to establish a realistic and systematic monetary compensation system that considers levels of competency as well as work experience.\u003c/p\u003e\u003cp\u003eThe group with the intention to stay demonstrated higher levels of job satisfaction related to opportunities for personal growth and tended to have full-time job status. Full-time and part-time job status is linked to job security, which has been repeatedly reported as a predictor of turnover intention [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Job security encompasses expectations regarding career advancement opportunities over time [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A lack of job security is associated with lower job satisfaction, weaker organizational commitment, poorer self-reported health and well-being, lower productivity, and higher turnover intention [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, a positive relationship was found between full-time/part-time status and the level of job satisfaction with the promotion system in the workplace. Specifically, full-time participants were more likely to remain employed when they were satisfied with the promotion system. Given the importance of promotion potential and professional development in decision-making about whether to stay or leave a job, offering more full-time positions for NGNs are likely contribute to longer retention.\u003c/p\u003e\u003cp\u003eThose with higher job satisfaction related to the promotion system were more likely to remain employed when they experienced lower levels of listlessness. This finding highlights that mental health plays a crucial role in job retention, even in the presence of professional achievements such as promotions. Numerous studies have found negative correlations between mental health status and turnover intention among nurses. Specifically, nurses with turnover intention reported significantly higher frequencies of depressive moods [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. NGNs often experience psychological consequences including depression, anxiety, and burnout due to insufficient training and excessive workloads [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These findings indicate that administrative support for NGNs should prioritize mental health management alongside fostering professional competency and maintaining an adequate workload.\u003c/p\u003e\u003cp\u003eThe experience of taking job-preparation classes during the undergraduate program emerged as an important predictor of turnover intention (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This finding is in line with previous studies suggesting that undergraduate nursing programs that effectively prepare students for clinical practice positively influence retention [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. A scoping review emphasized the essential values that undergraduate nursing programs need to cultivate to promote retention after qualification, including resilience, commitment, preparation for transition, and perceived knowledge and confidence regarding nursing skills [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. According to Kenny et al. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], NGNs who were satisfied with their undergraduate education in work preparation reported significantly higher job satisfaction. These findings underline the importance of undergraduate nursing programs in preparing students psychologically and intellectually for the transition into practice, which can enhance job satisfaction and retention during the early stages of their nursing careers.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDespite the significance of this study, there are a couple of limitations. First, due to the nature of cross-sectional data, the actual number of NGNs who left their jobs is not known, making it impossible to draw causal inferences. Longitudinal studies could be instrumental in identifying the causal relationships among variables and characteristics of NGNs who leave their workplaces in a short period of employment. Second, since this study was conducted in South Korea, caution is needed when generalizing the findings to other countries. However, the findings of this study highlight important reasons why current younger-generation nurses plan to leave their current workplaces based on a predictive model that considers various interactions among variables.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identified predictors of turnover intention among NGNs with less than one year of experience in the current workplaces by analyzing a large national dataset using a decision tree model. The findings of this study contribute to the development of effective human resource management strategies in the work environment for nurses, as well as educational initiatives in undergraduate nursing programs aimed at enhancing the retention of NGNs. To reduce turnover intention and prevent early turnover among NGNs, employers should create and provide various opportunities for promotion and personal development, along with monetary rewards and emotional support. Additionally, nurse educators should encourage students to engage actively in coursework that addresses professional nursing values and the organizational expectations they will encounter in the workplace.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was exempted from approval by the Institutional Review Board of Kyungpook National University in accordance with the Declaration of Helsinki (No. 2024-0009).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman ethics and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the Korea Employment Information Service (Graduates Occupational Mobility Survey 2016- 2020) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no financial assistance for conducting this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization; MM, Data curation; MM, HK, Formal analysis; MM, Investigation; MM, HK, Methodology; MM, Project administration; MM, Resources; MM, Supervision; MM, Validation; MM, HK, Visualization; MM, HK, Roles/Writing - original draft; MM, HK, Writing - review \u0026amp; editing: MM, HK.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMinistry of Health and Welfare. 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Predictors of turnover among new nurses using multilevel survival analysis. J Korean Acad Nurs. 2016;46(5):733-43. https://doi.org/10.4040/jkan.2016.46.5.733\u003c/li\u003e\n\u003cli\u003eAnselmo-Witzel S, Orshan SA, Heitner KL, Bachand J. Are Generation Y nurses satisfied on the job? Understanding their lived experiences. J Nurs Adm. 2017;47(4):232-7. https://doi.org/10.1097/NNA.0000000000000470\u003c/li\u003e\n\u003cli\u003eChristopher SA, Chiarella EM, Waters D. Can Generation Y nurses supply areas of shortage? New graduate challenges in today\u0026rsquo;s job market. Aust J Adv Nurs. 2015;33:35-44. https://doi.org/10.37464/2016.332.1549\u003c/li\u003e\n\u003cli\u003eLee T, Ji Y. Professional nursing values in nursing students during transitional period to nurses from the perspective of generation Z: A longitudinal study. J Adv Nurs. 2024;2024:jan.16637. https://doi.org/10.1111/jan.16637 \u003c/li\u003e\n\u003cli\u003eWang E, Hu H, Mao S, Liu H. Intrinsic motivation and turnover intention among geriatric nurses employed in nursing homes: roles of job burnout and pay satisfaction. Contemp Nurse. 2019;55(2-3):195-210. https://doi.org/10.1080/10376178.2019.1641120 \u003c/li\u003e\n\u003cli\u003eBeheshtifar M, Allahyary MH. Study the relationship among organizational reputation with organizational commitment and employees\u0026rsquo; turnover intention. Int Res J Appl Basic Sci. 2013;6(10):1467-78.\u003c/li\u003e\n\u003cli\u003eSokhanvar M, Kakemam E, Chegini Z, Sarbakhsh P. Hospital nurses\u0026apos; job security and turnover intention and factors contributing to their turnover intention: A cross-sectional study. Nurs Midwifery Stud. 2018;7(3):133-40. https://doi.org/10.4103/nms.nms_2_17\u003c/li\u003e\n\u003cli\u003eEmberland J, Rundmo T. Implications of job insecurity perceptions and job insecurity responses for psychological well-being, turnover intentions and reported risk behavior. Saf Sci. 2010;48:452-9. https://doi.org/10.1016/j.ssci.2009.12.002\u003c/li\u003e\n\u003cli\u003eLaine M, van der Heijden BI, Wickstr\u0026ouml;m G, Hasselhorn HM, Tackenberg P. Job insecurity and intent to leave the nursing profession in Europe. Int J Hum Resour Manag. 2009;20(2):420-38. https://doi.org/10.1080/09585190802673486\u003c/li\u003e\n\u003cli\u003eBakker EJ, Kox JH, Boot CR, Francke AL, van der Beek AJ, Roelofs PD. Improving mental health of student and novice nurses to prevent dropout: A systematic review. J Adv Nurs. 2020;76(10):2494-509. https://doi.org/10.1111/jan.14453\u003c/li\u003e\n\u003cli\u003eCollard SS, Scammell J, Tee S. Closing the gap on nurse retention: A scoping review of implications for undergraduate education. Nurse Educ Today. 2020;84:104253. https://doi.org/10.1016/j.nedt.2019.104253 \u003c/li\u003e\n\u003cli\u003eKenny P, Reeve R, Hall J. Satisfaction with nursing education, job satisfaction, and work intentions of new graduate nurses. Nurse Educ Today. 2016;36:230-5. https://doi.org/10.1016/j.nedt.2015.10.023\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"decision trees, nurses, personnel turnover, secondary data analysis","lastPublishedDoi":"10.21203/rs.3.rs-7082239/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7082239/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground.\u003c/strong\u003e The high turnover intention of newly graduated nurses has a multifaceted impact on the healthcare system. Analyzing large datasets using the machine learning methods can more accurately predict influencing factors of turnover intention in this population. This study aims to identify predictors of turnover intention among newly graduated nurses with less than one year of experience in current workplaces, using a decision tree model by analyzing 2016-2020 Graduate Occupational Mobility Survey conducted in South Korea.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods.\u003c/strong\u003e This is a secondary data analysis using a national large dataset. The data of 492 new nursing graduates were included in the analysis. Predictive variables for modeling were grouped into four categories: personal factors, workplace factors, college factors, and physical and mental health factors. Among these, the variables identified through univariate analysis were selected for the final analysis. The Chi-square Automatic Interaction Detection decision tree algorithm was implemented using SPSS Modeler.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults.\u003c/strong\u003e 23.6% (N=116) of participants reported turnover intention. The key predictors of turnover intention included lower levels of job satisfaction concerning personal development and social reputation related to the job, as well as the absence of incentive payments. Factors associated with a high intention for retention included greater satisfaction with personal growth and promotion systems, employment in permanent positions, holding full-time jobs, and experiencing fewer feelings of listlessness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Nursing administrators must endeavor to develop effective human resource management strategies that offer opportunities for self-development and career advancement, improve the social reputation of the institution, and ensure job security to mitigate early turnover intentions among newly graduated nurses. Additionally, integrating mental health management is crucial for enhancing workforce stability. In nursing colleges, developing educational strategies to prepare nursing students for organizational expectations and nursing competence will contribute to improving retention within this population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: \u003c/strong\u003enot applicable.\u003c/p\u003e","manuscriptTitle":"Predicting Turnover Intention Among Newly Graduated Nurses in South Korea: A Decision Tree Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 05:52:13","doi":"10.21203/rs.3.rs-7082239/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-18T07:10:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-16T11:27:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31433729807771400687125890023533498967","date":"2025-08-07T17:46:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-24T21:57:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T09:34:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139704866414582404159502577351449012537","date":"2025-07-21T04:03:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176851779298872966007010709574209787392","date":"2025-07-18T14:41:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-16T09:51:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-14T06:27:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-11T07:10:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-11T07:09:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nursing","date":"2025-07-09T09:11:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nursing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurs","sideBox":"Learn more about [BMC Nursing](http://bmcnurs.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurs/default.aspx","title":"BMC Nursing","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"46d8a989-ab32-4077-805b-5d45d73d69dc","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T16:07:55+00:00","versionOfRecord":{"articleIdentity":"rs-7082239","link":"https://doi.org/10.1186/s12912-025-04041-5","journal":{"identity":"bmc-nursing","isVorOnly":false,"title":"BMC Nursing"},"publishedOn":"2025-11-18 15:59:01","publishedOnDateReadable":"November 18th, 2025"},"versionCreatedAt":"2025-07-23 05:52:13","video":"","vorDoi":"10.1186/s12912-025-04041-5","vorDoiUrl":"https://doi.org/10.1186/s12912-025-04041-5","workflowStages":[]},"version":"v1","identity":"rs-7082239","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7082239","identity":"rs-7082239","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}