Development and validation of a machine learning-based predictive model for compassion fatigue in nursing interns: A cross-sectional study with latent profile analysis

Development and validation of a machine learning-based predictive model for compassion fatigue in nursing interns: A cross-sectional study with latent profile 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 Development and validation of a machine learning-based predictive model for compassion fatigue in nursing interns: A cross-sectional study with latent profile analysis Lijuan Yi, Ting Shuai, Yi Liu, Jingjing Zhou, Maria F. Jiménez-Herrera, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4709842/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Dec, 2024 Read the published version in BMC Medical Education → Version 1 posted 10 You are reading this latest preprint version Abstract Background Exposure to compassion fatigue during internships can significantly impact on nursing students’ future career trajectories and their intention to stay in the nursing profession. Accurately identifying nursing students at high risk of compassion fatigue is vital for timely interventions. However, existing assessment tools often fail to account for within-group variability and lack predictive capabilities. To develop and validate a predictive model for detecting the risk of compassion fatigue among nursing students during their placement. Design: A cross-sectional study design. Methods Data from 2256 nursing students in China between December 2021 and June 2022 were collected on compassion fatigue, professional identity, self-efficacy, social support, psychological resilience, coping styles, and demographic characteristics. The latent profile analysis was performed to classify compassion fatigue levels of nursing students. Univariate analysis, least absolute shrinkage and selection operator regression analysis were conducted to identify potential predictors of compassion fatigue. Eight machine learning algorithms were selected to predict compassion fatigue, and the performance of these machine learning models were evaluated using calibration and discrimination metrics. Additionally, the best-performing model from this evaluation was selected for further independent assessment. Results A three-profile model best fit the data, identifying low (55.73%), moderate (32.17%), and severe (12.10%) profiles for compassion fatigue. The area under the curve values for the eight machine learning models ranged from 0.644 to 0.826 for the training set and from 0.651 to 0.757 for the test set. The eXtreme Gradient Boosting performed best, with area under the receiver operating characteristic curve values of 0.840, 0.768, and 0.731 in the training, validation, and test sets, respectively. SHAP analysis clarified the model’s explanatory variables, with psychological resilience, professional identity, and social support being the most significant contributors to the risk of compassion fatigue. A user-friendly, web-based prediction tool for calculating the risk of compassion fatigue was developed. Conclusions The eXtreme Gradient Boosting classifier demonstrates exceptional performance, and clinical implementation of the online tool can provide nursing managers with an effective means to manage compassion fatigue. Nursing student Internship nonmedical Compassion fatigue Prediction model Latent profile analysis Machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Nursing is a profession defined by its commitment to compassion, but it is also accompanied by significant stress. Nurses frequently witness patients’ suffering and distress while providing care and support. Prolonged exposure to such traumatic experiences can result in psychological distress, impairing their capacity to deliver optimal care ( 1 – 3 ). This phenomenon, known as compassion fatigue, was first defined by Joinson in 1992, and it encompasses physical, psychological, and social dysfunction arising from continuous exposure to patient suffering or traumatic events ( 4 ). Compassion fatigue comprises two elements: burnout and secondary traumatic stress ( 5 , 6 ). This emotional and psychological exhaustion leads to various health issues among nurses, including musculoskeletal disorders, sleep disturbances, depression, and reduced professional identity and work engagement ( 7 – 10 ). Compassion fatigue is not exclusive to registered nurses; it also affects nursing students ( 11 – 14 ). In China, pre-licensure nursing education includes both associate (three or five-year programs) and baccalaureate (four-year programs) tracks. These programs emphasize a combination of theoretical knowledge and practical skills. After completing on-campus theoretical studies, students are required to undertake at least eight months of clinical placement in secondary or higher-level hospitals. During this clinical placement period, these students are referred to as nursing interns. A survey of 972 Chinese nursing interns revealed that 97.8% experienced moderate burnout, and 55.3% suffered from secondary traumatic stress ( 15 ). High levels of compassion fatigue or burnout are associated with a higher intention to drop out, increasing actual attrition rates ( 16 – 20 ). Additionally, burnout developed during educational programs may persist after graduation, impacting the health and retention of new nurses ( 20 , 21 ). Thus, compassion fatigue or burnout adversely affects the career development of nursing students. Early identification and intervention for those at risk of compassion fatigue are crucial to prevent the worsening of symptoms. Current assessments of compassion fatigue primarily rely on surveys ( 22 ). Researchers such as Figley and Stamm have developed several measurement tools, including the Compassion Fatigue Self-Test ( 23 ), the Compassion Satisfaction and Fatigue Scale ( 24 ), the Compassion Fatigue Scale ( 25 ), the Compassion Fatigue Short Scale ( 26 ) and the Professional Quality of Life Scale ( 27 ). While these tools are essential for assessing compassion fatigue, they have limitations. For instance, some tools consider compassion fatigue as a combination of burnout and secondary traumatic stress or focus on a single dimension, complicating the assessment process ( 22 – 25 , 27 ). Additionally, the Compassion Fatigue Short Scale, despite providing a straightforward measurement method, lacks clear cutoff values, making it difficult to distinguish between low-risk and high-risk individuals ( 26 , 28 ). More importantly, the aforementioned tools typically assess compassion fatigue among nursing interns using total scores, which overlooks individual differences and are primarily designed for assessment rather than prediction( 15 , 16 , 29 , 30 ). Consequently, there is an urgent need for a new method to effectively assess and predict the risk of compassion fatigue among nursing interns. Several psychosocial factors, such as social support, coping strategies, self-efficacy, psychological resilience, and professional identity, are closely related to compassion fatigue among nursing students ( 15 , 16 , 31 , 32 ). Studies have shown that educational background and career-related factors, such as academic major, program length, previous student leadership, and career intentions, are significantly associated with compassion fatigue or burnout risk ( 15 , 31 , 33 , 34 ). Demographic factors like gender, residence, only-child status, and monthly expenses, as well as internship-related characteristics such as the level of the internship hospital and the frequency of night shifts, are also associated with compassion fatigue or burnout ( 32 , 33 , 35 – 37 ). These factors should be considered when assessing nursing students at potential risk. Compassion fatigue is a complex and dynamic phenomenon. Traditional “one-size-fits-all” approaches are insufficient to convey its impact on different psychological profiles. Existing cross-sectional surveys on compassion fatigue among nursing students typically use variable-centered approaches, measuring compassion fatigue through total scores from scales or subscales, neglecting within-group variability ( 15 , 16 , 31 , 32 , 38 ). To reveal the heterogeneity of compassion fatigue among nursing students more comprehensively and precisely and to identify characteristics of different profiles, combining latent profile analysis (LPA) with machine learning could be a potential solution. LPA is a person-centered statistical method that classifies nursing students into different risk groups based on the measurement data of compassion fatigue, providing a basis for tailored interventions ( 39 , 40 ). Unlike traditional cutoff-based methods, LPA identifies unobserved heterogeneity within the population based on individuals’ responses to continuous variables, grouping those with similar response patterns into homogeneous subgroups ( 41 ). This approach addresses the issue of neglecting within-group variability when using total scores, making LPA a superior choice ( 41 , 42 ). However, LPA lacks predictive capabilities, necessitating its combination with other methods for predictive functions. Machine learning, a technique for learning patterns from data and making predictions, possesses strong data processing and prediction capabilities ( 43 ). In compassion fatigue research, machine learning can construct predictive models based on LPA classification results and other related variables, enabling accurate predictions of individual risk levels. Therefore, combining LPA with machine learning can provide a more comprehensive assessment and prediction of compassion fatigue risk among nursing interns. The main objectives of this study are: ( 1 ) to identify potential classifications of compassion fatigue among nursing interns using LPA; ( 2 ) to develop and validate machine learning models for predicting individual risk levels; and ( 3 ) to develop an online prediction tool for practical application. Methods Study design This cross-sectional study was conducted from December 2021 to June 2022 among nursing interns at 10 public junior colleges in Hunan Province, China. This design was chosen to provide a comprehensive overview of the population at a specific time point. The study adheres to guidelines outlined in the STROBE and TRIPOD statements. Participants Participants were recruited using a convenience sampling method. Inclusion criteria were: ( 1 ) enrollment in a three- or five-year associate nursing program, ( 2 ) active participation in a clinical internship lasting at least eight months in a secondary-level or above hospital, and ( 3 ) willingness to participate with informed consent. Nursing interns whose clinical practice was in clerical management or administration, without direct patient contact, were excluded. Sample size Larger sample sizes are typically used in survey research to achieve more accurate and stable results ( 44 ). However, there is no standardized method for calculating sample size in survey studies using machine learning techniques ( 45 ). For LPA, a sample size of more than 500 cases is recommended to ensure accuracy ( 46 ). Furthermore, a review by Spurk et al. revealed that 53.4% of studies used sample sizes greater than 500, further supporting the appropriateness of this rule of thumb ( 47 ). Data collection Full-time student counselors responsible for managing nursing students during their internships, were trained as research assistants. They provided standardized explanations of the purpose, risks, and benefits of this study. Data were collected via online surveys created on the WenJuanXing platform ( https://www.wjx.cn ), shared through WeChat groups. Each IP address was restricted to one submission. To reduce missing data and ensure data quality, the online survey required all questions to be answered. Participants’ anonymity and voluntary participation were emphasized, and participants could withdraw at any time. Surveys with identical or patterned responses were excluded. After removing 149 invalid responses, the final sample consisted of 2256 participants, resulting in an effective response rate of 93.8%. Instruments General demographic information collection table A self-designed table collected sociodemographic data including gender, academic major, program length, place of residence, only-child status, monthly expenditure, previous experience as a student leader, hospital level during internship, number of night shifts per month, and career intentions. The Compassion Fatigue Short Scale The Compassion Fatigue Short Scale, developed by Adams et al., comprises 13 items measuring secondary traumatic stress (5 items) and job burnout (8 items) ( 26 ). Responses range from 1 (never) to 10 (very often), yielding a total score ranging from 13 to 130, where higher scores indicate greater levels of compassion fatigue. The Chinese version, translated and validated by Sun (2015) ( 28 ), demonstrated good reliability and validity, with Cronbach’s alpha coefficients ranging from 0.87 to 0.95. In this study, the overall Cronbach’s alpha coefficient of the Chinese version was 0.92. The Professional Identity Scale Professional identity was measured using the Professional Identity Scale by Brown et al., consisting of 10 items rated on a 5-point scale from 1 (never) to 5 (always) ( 48 ). Higher scores indicate stronger professional identity. The Chinese version, translated and validated by Lu et al., achieved a Cronbach's alpha coefficient of 0.82 ( 49 ). In this study, the overall Cronbach's alpha coefficient of the Chinese version was 0.80. The General Self-efficacy Scale The General Self-Efficacy Scale, developed by Schwarzer and Jerusalem (1995) ( 50 ) and adapted for Chinese population by Zeng et al. ( 51 ), includes 10 items rated from 1 (not true at all) to 4 (exactly true), with total scores ranging from 10 to 40, where higher scores indicate greater self-efficacy. Zeng et al.'s ( 51 ) research demonstrated that the scale exhibits good internal consistency and criterion validity, with an overall Cronbach's alpha coefficient of 0.89 in this study. The Perceived Social Support Scale The Perceived Social Support Scale, developed by Zimet et al. ( 52 ), translated by Jiang ( 53 ) and modified by Yan et al. ( 54 ), includes 12 items across three dimensions: family support (items 3, 4, 8, 11), friend support (items 6, 7, 9, 12), and other support (items 1, 2, 5, 10). Responses range from 1 (strongly disagree) to 7 (strongly agree), resulting in a total score ranging from 12 to 84. Higher scores indicate higher levels of perceived social support. In Yan et al.'s study, the scale demonstrated internal consistency and test-retest reliability coefficients of 0.87 and 0.85, respectively ( 54 ). In the present study, the Chinese version of the scale achieved an overall Cronbach’s alpha coefficient of 0.92. The 10-item Connor-Davidson Resilience Scale The 10-item Connor-Davidson Resilience Scale is a refinement by Campbell-Sills of the original scale developed by Connor and Davidson ( 55 , 56 ). This unidimensional scale comprises 10 items, each rated on a 5-point scale (0–4), with higher scores indicating greater resilience. In this study, we utilized the Chinese version of the scale, translated and validated by Ye in 2016 ( 57 ). In a sample of Chinese nursing students, this scale accounted for 48.641% of the total variance, with a Cronbach's α coefficient of 0.851, demonstrating good reliability and validity ( 34 ). In the present study, the overall Cronbach's α coefficient for the scale was 0.93. Simple Coping Style Questionnaire The simple coping style questionnaire was adapted and translated by Xie et al. based on Folkman and Lazarus' Ways of Coping Questionnaire ( 58 , 59 ). The 20-item scale comprises two dimensions: positive coping styles (items 1–12) and negative coping styles (items 13–20). Each item uses a Likert four-point scale, ranging from 1 (do not take) to 4 (often take). Higher scores in the positive coping style dimension indicate a greater likelihood of adopting positive coping strategies, while higher scores in the negative coping style dimension indicate a greater likelihood of adopting negative coping strategies. The scale has demonstrated good reliability and validity among Chinese populations, with a Cronbach’s α coefficient of 0.90 ( 58 ). In the present study, the scale achieved an overall Cronbach’s α coefficient of 0.87. Statistical analyses Descriptive and univariate analysis Descriptive statistics summarized the general characteristics of the participants. Continuous variables, which did not follow a normal distribution, were represented as median and inter-quartile range, while categorical variables were shown as numbers and percentages. Univariate analysis, using the Mann-Whitney U test, chi-squared test, or Fisher’s exact test as appropriate, identified predictive factors for compassion fatigue or burnout. The candidate factors considered included demographic characteristics, professional identity, self-efficacy, social support, psychological resilience, and coping styles. Analyses were performed using SPSS 26.0 (IBM Corp., Armonk, NY, USA), with a significance threshold of P < 0.05 for two-sided test. Latent profile analysis LPA was conducted using the 13 items from the Compassion Fatigue Short Scale as indicators, employing robust maximum likelihood estimation to identify subgroups of compassion fatigue symptoms. Various models were compared based on entropy, the Lo-Mendell-Rubin likelihood ratio test (LMR), the bootstrap likelihood ratio test (BLRT), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and adjusted Bayesian Information Criterion (aBIC) ( 60 – 62 ). Specifically, an entropy value > 0.80 indicated that the latent classes were highly discriminative, and significant p-values for the LMR and BLRT suggested that the k-class model was preferable to the k-1 class model ( 60 ). Additionally, lower values of AIC, BIC, and aBIC indicated better model fit, while the “turning point” in the scree plot for aBIC suggested the appropriate number of classes ( 61 , 63 ). Ultimately, the number of latent profiles was determined by a combination of these fit criteria. Analyses were conducted using Mplus version 8.3 ( 64 ). To facilitate the construction of the subsequent predictive model, following recommendation from previous studies ( 64 , 65 ), we assigned individuals who belonged to the latent profile representing the lowest level of symptoms or risks as “non-cases,” while other individuals were considered “cases.” Features selection After determining the optimal model and defining classifications, we assessed differences in some sociodemographic characteristics, professional identity, self-efficacy, social support, psychological resilience, and coping styles between the “non-cases” and “cases” groups. Significant variables from the univariate analysis ( P ≤ 0.05) were further analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression, a technique that penalizes regression coefficients to optimize the model by retaining only significant predictors( 66 ). This approach handles complex covariance structures and improves predictive accuracy. LASSO regression was performed using Lasso CV with 10-fold cross-validation. Development and evaluation of machine learning models learning Eight machine learning algorithms were used to develop and compare models for predicting the risk of compassion fatigue, including logistic regression, support vector machine, random forest, multi-layer perceptron, extreme gradient boosting (XGBoost), gradient boosting decision trees, Gaussian naive Bayes, and adaptive boosting. All models were implemented using Python 3.7, with the "xgboost 2.0.1" package for XGBoost and the "scikit-learn 1.1.3" package for the remaining algorithms. Models were trained and validated using bootstrap resampling, with a 7:3 training-to-testing ratio. Performance was evaluated using metrics such as area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Calibration was assessed by comparing predicted and observed incidence of “cases.” Model optimization and evaluation To ensure robustness and mitigate overfitting, 10-fold cross-validation assessed predictive performance. The dataset was randomly divided into ten equal parts, with training and validation repeated five times. Model discrimination was evaluated using receiver operating characteristic (ROC) analysis and quantified by AUC. Calibration plots assessed the agreement between predicted probabilities and actual outcomes. Decision curve analysis (DCA) estimated the clinical utility and net benefit. Feature importance was assessed using SHAP analysis, with higher absolute SHAP values indicating greater impact on predictions ( 67 ). Additionally, we explored the distribution of feature values and their relationship with model predictions to gain further insights into model behavior. SHAP analysis was conducted using the “shap 0.43.0” package. Results Descriptive statistics Data were collected from 2256 nursing interns. Most participants were female (2077, 92.07%), came from rural areas (1784, 79.08%), and were non-only children (1962, 86.97%). Regarding economic status, 69.77% had a monthly expenditure between 1000 and 2000 RMB (1574, 69.77%). A significant majority were enrolled in nursing programs (1660, 73.58%), with most of them in three-year programs (1736, 76.95%). Approximately half had experience serving as student leaders (1172, 51.95%). In terms of internship conditions, 75.71% interned at tertiary hospitals (1708, 75.71%), and most worked night shifts 3 to 4 times per month (1037, 45.96%). Career intention surveys showed that 86.13% intended to pursue a nursing career (1943, 86.13%). Psychological assessments revealed median scores of 39.00 for professional identity, 2.800 for self-efficacy, 58.00 for social support, 23.00 for psychological resilience, and 34.00 for coping style. Further details of participants’ characteristics are presented in Table 1 . Table 1 Univariate analysis of influencing factors of compassion fatigue of nursing interns Variable N (%) Non-case a Case b Statistics p Academic major, n (%) 15.358 < 0.001 Nursing 1660 (73.58) 973 (76.80) 687 (69.46) Midwifery 596 (26.42) 294 (23.20) 302 (30.54) Length of schooling, n (%) 1.452 0.228 3-year 1736 (76.95) 963 (76.01) 773 (78.16) 5-year 520 (23.05) 304 (23.99) 216 (21.84) Gender, n (%) 14.759 < 0.001 Male 179 (7.93) 125 (9.87) 54 (5.46) Female 2077 (92.07) 1142 (90.13) 935 (94.54) Residence, n (%) 0.167 0.683 Urban 472 (20.92) 269 (21.23) 203 (20.53) Rural 1784 (79.08) 998 (78.77) 786 (79.47) Only child or not, n (%) 0.988 0.320 Yes 294 (13.03) 173 (13.65) 121 (12.24) No 1962 (86.97) 1094 (86.35) 868 (87.76) Monthly expenses, n (%) 0.447 0.800 <1000 RMB 420 (18.62) 242 (19.10) 178 (18.00) 1000–2000 RMB 1574 (69.77) 879 (69.38) 695 (70.27) 2001–3000 RMB 262 (11.61) 146 (11.52) 116 (11.73) Whether as a student cadre, n (%) 0.035 0.851 Yes 1172 (51.95) 656 (51.78) 516 (52.17) No 1084 (48.05) 611 (48.22) 473 (47.83) Hospital level during internship, n (%) 6.498 0.011 Tertiary 1708 (75.71) 985 (77.74) 723 (73.10) Secondary 548 (24.29) 282 (22.26) 266 (26.90) Frequency of night-shift per month, n (%) 8.868 0.031 0–2 803 (35.60) 468 (36.94) 335 (33.87) 3–4 1037 (45.96) 590 (46.57) 447 (45.20) 5–6 278 (12.32) 145 (11.44) 133 (13.45) >6 138 (6.12) 64 (5.05) 74 (7.48) Career intention, n (%) 79.820 < 0.001 Yes 1943 (86.13) 1164 (91.87) 779 (78.77) No 313 (13.87) 103 (8.13) 210 (21.23) Professional identity, median (IQR) 39.00 (34.00, 43.00) 41.00 (37.00, 44.00) 36.00 (31.00, 40.00) 17.774 < 0.001 Self-efficacy, median (IQR) 2.800 (2.50, 3.000) 2.80 (2.60, 3.00) 2.70 (2.40, 3.00) 10.160 < 0.001 Social support, median IQR) 58.00 (50.00, 66.00) 61.00 (53.00, 69.00) 54.00 (48.00, 61.00) 13.058 < 0.001 Psychological resilience, median (IQR) 23.00 (20.00, 29.00) 26.00 (21.00, 30.00) 20.00 (18.00, 25.00) 16.793 < 0.001 Coping style, median (IQR) 34.00 (27.00,4 0.00) 34.00 (27.00, 40.00) 33.00 (27.00, 39.00) 2.691 0.007 Note : IQR, Interquartile spacing; CF, compassion fatigue. a:"non-cases" refers to nursing interns with low levels of compassion fatigue; b: "cases" refers to nursing interns with moderate to severe levels of compassion fatigue. Latent profiles of nursing students’ compassion fatigue LPA was performed with one to four latent classes, and the fit indices for these models are shown in Table 2 . All classifications had entropy values exceeding 0.9. The BLRT was statistically significant across all models, while the LMR was significant only for the one to three-class models. With an increase in the number of classes, the AIC, BIC, and aBIC values decreased, and the scree plot of aBIC flattened after the three-class model (Fig. 1 ). Considering statistical criteria, interpretability, and parsimony, the three-class model was selected as the optimal model. Table 2 Goodness of Fit Statistics for 1 to 4 Class Models Note : AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; aBIC, adjusted Bayesian Information Criterion; LMR, Lo–Mendell–Rubin; BLRT, bootstrap likelihood ratio test; n.a., not applicable. Class AIC BIC aBIC Entropy LMR(p) BLRT(p) Sample proportion (%) per class 1 135405.166 135553.921 135471.315 n.a. n.a. n.a. n.a. 2 125201.556 125430.410 125303.324 0.935 < 0.001 < 0.001 71.37/28.63 3 122468.063 122777.016 122605.449 0.908 < 0.001 < 0.001 55.73/32.17/12.10 4 121328.755 121717.807 121501.759 0.916 0.3132 < 0.001 25.67/55.70/8.23/10.40 Table 3 shows the average latent class probabilities for the most likely latent class membership in the three-class model (0.968, 0.940, and 0.960), indicating reasonable classification and good distinction. The distribution and conditional means of the Compassion Fatigue Short Scale items for each class in the three-class model are illustrated in Fig. 2 . Based on the conditional means of the items for each class, Class 1 (n = 1257, 55.73%) was defined as the “low compassion fatigue” group, Class 2 (n = 726, 32.17%) as the “moderate compassion fatigue” group, and Class 3 (n = 273, 12.10%) as the “severe compassion fatigue” group. Table 3 Average latent class probabilities for most likely latent class membership by latent class Latent class Latent class membership 1 2 3 1 0.968 0.032 0.000 2 0.043 0.940 0.018 3 0.000 0.040 0.960 Influencing factors of nursing students’ compassion fatigue To explore influencing factors and develop a risk prediction model for compassion fatigue among nursing interns, participants in the “low compassion fatigue” group identified by LPA were categorized as “non-cases” (i.e., without compassion fatigue), and those in the “moderate compassion fatigue” and “severe compassion fatigue” groups were categorized as “cases” (i.e., potentially experiencing compassion fatigue). Univariate analysis revealed statistically significant differences between the “non-case” and “case” groups in terms of major (χ 2 = 15.358, P < 0.001), gender (χ 2 = 14.759, P < 0.001), level of internship hospital (χ2 = 6.498, P = 0.011), frequency of night shifts per month (χ 2 = 8.868, P = 0.031), career intention (χ 2 = 79.820, P < 0.001), professional identity (z = 17.774, P < 0.001), self-efficacy (z = 10.160, P < 0.001), social support (z = 13.058, P < 0.001), psychological resilience (z = 16.793, P < 0.001), and coping style (z = 2.691, P = 0.007) (see Table 1 ). These 10 variables were included in the LASSO model. Using 10-fold cross-validation, we identified 9 key features for developing the prediction model: major, gender, frequency of night shifts per month, career intention, professional identity, self-efficacy, social support, psychological resilience, and coping style (see Fig. 3 ). Comparison of multiple classification models We compared the performance of eight machine learning classification models for predicting the risk of compassion fatigue among nursing students during their internship. The XGBoost model demonstrated high stability and accuracy in both the training and validation sets, outperforming others with its AUC, accuracy, and F1 scores. In contrast, the Random Forest model showed perfect performance in the training set but significant overfitting, resulting in a notable decline in the validation set. Other models, such as Logistic Regression, Support Vector Machine, and Multi-Layer Perceptron, showed relatively weaker performance across various metrics. Results are summarized in Table 4 , and Fig. 4 provides a comprehensive visualization of the ROC curves for all models in the training and validation sets (Figs. 4 A and 4 B), the calibration plots (Fig. 4 C), and the forest plot with the AUC score results (Fig. 4 D). Table 4 Predictive performance of the eight machine learning techniques in the training and validation sets for compassion fatigue of nursing interns Models AUC Accuracy Sensitivity Specificity PPV NPV F1score Training set XGBoost 0.826 0.748 0.771 0.731 0.697 0.797 0.732 LR 0.762 0.700 0.663 0.730 0.663 0.737 0.659 RF 1.000 0.999 1.000 1.000 1.000 0.999 1.000 AdaBoost 0.788 0.709 0.731 0.694 0.657 0.768 0.688 GBDT 0.778 0.707 0.711 0.699 0.665 0.742 0.687 GNB 0.744 0.686 0.712 0.667 0.631 0.746 0.667 MLP 0.719 0.654 0.703 0.617 0.587 0.729 0.639 SVM 0.644 0.611 0.677 0.562 0.548 0.688 0.605 Validation set XGBoost 0.750 0.679 0.643 0.730 0.613 0.738 0.627 LR 0.772 0.707 0.710 0.709 0.665 0.747 0.684 RF 0.749 0.682 0.696 0.684 0.708 0.673 0.701 AdaBoost 0.757 0.677 0.668 0.722 0.608 0.751 0.635 GBDT 0.749 0.679 0.624 0.753 0.638 0.712 0.630 GNB 0.738 0.671 0.661 0.711 0.603 0.738 0.630 MLP 0.704 0.637 0.696 0.610 0.577 0.707 0.630 SVM 0.651 0.612 0.694 0.552 0.548 0.685 0.611 Note : AUC, area under the curve; PPV, positive predictive value; NPV, negative predictive value; LR, logistic regression; SVM, support vector machine; RF, random forest; GBDT, gradient boosting decision tree; MLP, multi-layer perceptron; XGBoost, extreme gradient boosting; GNB, Gaussian naive Bayes; AdaBoost, adaptive boosting. Model optimization The XGBoost model, identified as the best performer in predicting compassion fatigue risk, was further optimized through hyper-parameter tuning and selected 9 pre-identified variables as input features. A 10-fold cross-validation strategy was employed, dividing participants into 10 groups, with each group serving as the test set in turn, while the remaining 9 groups were used for training and validation. Figures 5 A-C display the ROC curves and AUC values of the XGBoost model in the training, validation, and test sets, which were 0.840, 0.768, and 0.731, respectively, indicating good predictive capability (Table 5 and Fig. 5 A-C). Before evaluating the model's accuracy, we generated a learning curve. As shown in Fig. 5 D, the error between the training set and the validation set gradually converged as the training samples increased, indicating no significant overfitting. We then assessed the model's accuracy using a calibration plot (Fig. 5 E), which demonstrated that the predicted probabilities of the XGBoost model were highly consistent with the actual incidence of "cases." Finally, decision curve analysis (DCA) (Fig. 5 F) revealed that the model provided significant net benefits within a risk threshold below 70% compared to the “treat all” or “treat none” strategies, further demonstrating its effectiveness and practicality in real-world applications. Table 5 Performance of the XGBoost classification model for predicting the risk of compassion fatigue of nursing interns Models AUC Accuracy Sensitivity Specificity PPV NPV F1 score Training set 0.840 0.761 0.720 0.793 0.730 0.788 0.723 Validation set 0.768 0.704 0.653 0.789 0.660 0.741 0.653 Test set 0.731 0.656 0.770 0.581 0.607 0.704 0.679 Note : AUC, area under the curve; PPV, positive predictive value; NPV, negative predictive value. XGBoost, extreme gradient boosting. Model interpretation Figure 6 illustrates the impact of various features on the prediction outcomes of the model. As shown in Fig. 6 A, the SHAP summary plot, indicates that psychological resilience, professional identity, and social support are the features with the greatest influence on the model’s output. Figure 6 B, the bar chart of mean absolute SHAP values, further confirms this finding. Figures 6 C and 6 D decompose the feature contributions for two sample students, demonstrating the individual-level impact of these features on risk prediction. These figures clearly identify which feature values significantly affect the final risk prediction scores and cause them to fluctuate. Development of a web-based application for predicting compassion fatigue We developed a web-based application based on the final model’s predicted risks (available at http://www.xsmartanalysis.com/model/list/predict/model/html?mid= 15239&symbol = 21Dx71Ab62wF580Bl642 ), facilitating accurate individual risk assessment (Fig. 7 ). Discussion Latent profile analysis of compassion fatigue among nursing students during the internship This study utilized LPA to categorize compassion fatigue levels among nursing interns. Based on model fit criteria, the optimal model identified three profiles: “low compassion fatigue,” “moderate compassion fatigue,” and “severe compassion fatigue.” The “low compassion fatigue” group, comprising 55.73% of the participants, had average scores of 18.74 for burnout and 10.14 for secondary traumatic stress, with an overall average score of 28.874 on the Compassion Fatigue Short Scale. Although these nursing interns showed low overall compassion fatigue, they scored higher on item 2 (“I feel I have not accomplished much in my life”) and item 7 (“I often feel weak, tired, or exhausted as a caregiver”). These scores reflect their emotional and psychological adaptation to new environments and work challenges, likely due to the high intensity of internship tasks, academic pressure, and the fear of incompetence during the transition to their new roles ( 68 , 69 ). Despite the overall low level of compassion fatigue, the higher scores on specific items warrant attention to prevent progression to moderate or severe levels of fatigue. The “moderate compassion fatigue” group included 32.17% of the participants, with average scores of 37.78 for burnout and 19.14 for secondary traumatic stress, totaling 56.92 on the Compassion Fatigue Short Scale. This group also scored highest on items 2 and 7. Besides, item 9 (“I feel frustrated with my job”) showed the largest score difference, indicating professional confusion, fatigue, and emotional distress. This may stem from heightened sensitivity to patient suffering and death, leading to increased burnout and frustration ( 70 ). Additionally, the inability to establish stable relationships with supervisors and staff during a short internship exacerbates their emotional burden ( 71 ). Therefore, mindfulness training to enhance self-awareness and emotional management, along with a well-established professional and peer support system to provide continuous guidance and emotional support, is crucial to prevent the development of higher levels of compassion fatigue ( 72 , 73 ). The “severe compassion fatigue” group, representing 12.10% of the participants, had average scores of 55.92 for burnout and 32.83 for secondary traumatic stress, with a total score of 88.75. This group scored highest on items 2 and 7, and showed the largest score differences on item 8 (“I have intrusive thoughts about the traumatic situations I have encountered”) and item 10 (“I relive traumatic experiences when helping those in crisis”), indicating rapidly increasing psychological trauma and distress. These interns lack effective coping strategies and psychological resilience, leading to higher secondary traumatic stress and compassion fatigue ( 74 , 75 ). Long-term high-pressure environments and cumulative emotional burdens further aggravate these issues ( 75 ). Therefore, psychological counseling, support groups, stress management and coping skills training, and crisis peer support workshops are crucial for managing these high-pressure environments and traumatic situations( 76 ). Influencing factors of compassion fatigue among nursing students during the internship Demographic characteristics Our study identified several demographic factors associated with higher compassion fatigue: being female, specializing in midwifery, having frequent night shifts, and intending to switch to another profession after graduation. Female students reported higher compassion fatigue than males. Sacco et al. ( 77 ) found similar results among critical care nurses. Female students generally possess higher emotional intelligence and empathy( 78 ), making them emotionally engaged with patients, leading to emotional exhaustion and compassion fatigue over time ( 79 , 80 ). Additionally, cultural norms in China discourage emotional expression in males, making male students less likely to engage deeply emotionally, reducing their risk of compassion fatigue ( 78 ). Midwifery students reported higher compassion fatigue than nursing students. Despite working in generally positive obstetric environments, midwifery students frequently encounter complex or critical childbirth situations and neonatal deaths, leading to significant emotional strain ( 81 , 82 ). They also form strong empathetic bonds with patients, increasing their risk of secondary traumatic stress ( 76 , 83 ). Early emphasis on normal physiological birth may mislead students about the challenges of adverse events, resulting in increased emotional pressure and fatigue ( 84 ). Students with frequent night shifts reported higher compassion fatigue scores, consistent with findings among hospice nurses ( 85 ). Night shifts increase clinical knowledge and independence but also cause constant stress, emotional exhaustion, and exploitation or bullying, leading to anxiety and depression ( 85 ). Students intending to leave the profession after graduation reported higher compassion fatigue. This lack of intrinsic motivation and misalignment between expectations and reality during internships leads to emotional exhaustion and moral distress ( 86 ). Educators should encourage reflection on the nursing role to establish realistic career expectations and provide necessary support ( 87 ). Psychological resilience Psychological resilience was found to be a protective factor against compassion fatigue. Higher psychological resilience was associated with a lower risk of developing compassion fatigue, aligning with previous research ( 15 , 88 ). Psychological resilience enhances nursing students’ psychosocial functioning and professional performance ( 88 , 89 ), avoids emotion-centered coping, and builds confidence in managing workplace stress, thereby mitigating compassion fatigue and facilitating adaptation to the clinical environment ( 90 ). As advocated by the American Association of Colleges of Nursing, nursing education should emphasize resilience development to improve overall well-being of nursing students ( 91 ). Professional identity Higher professional identity was associated with lower compassion fatigue. This negative correlation between compassion fatigue and professional identity has also been validated in similar groups ( 92 ). Professional identity acts as a psychological resource, helping nursing students maintain a positive attitude during high-pressure and high-risk internship tasks and reducing negative emotional impact ( 93 ). Decreased professional identity leads to reduced work enthusiasm and satisfaction, increasing susceptibility to compassion fatigue ( 94 , 95 ). Social support Greater social support was linked to lower risk of compassion fatigue, consistent with findings reported by a study involving 307 intern nursing and midwifery students ( 14 ). Social support enhances resilience, alleviates stress, loneliness, and anxiety ( 96 – 98 ). According to the stress coping model, social support is a crucial external resource for coping with stress ( 99 ). When students receive support from family, friends, and mentors, they may gain confidence and courage to employ positive coping strategies, thus mitigating negative emotions and preventing compassion fatigue ( 100 ). Therefore, it is essential to develop and refine social skills development strategies related to support networks for nursing students, guiding them to actively seek social support when facing internship pressures. Coping style Negative coping strategies significantly and positively predict higher compassion fatigue, while positive coping strategies negatively predict lower compassion fatigue. Similar findings have also been validated in Rui's study on clinical nurses ( 101 ). Coping strategies influence occupational stress and mental health ( 102 ). The demanding internship tasks and academic pressures consume a substantial amount of energy, and as resources are continuously depleted, negative emotions gradually emerge ( 103 ). Nursing students who adopt negative coping strategies are more likely to develop negative thoughts and avoidance behaviors, thereby increasing the risk of compassion fatigue ( 104 ). In contrast, positive coping strategies help nursing students maintain a positive attitude and mental health by effectively managing stressful events and mobilizing resources, thereby mitigating the impact of compassion fatigue ( 102 , 104 ). Self-efficacy Higher self-efficacy was associated with lower compassion fatigue, consistent with findings in other nurse populations ( 105 , 106 ). Self-efficacy fosters a positive perception of nursing, stronger professional identity, and better career preparation ( 107 , 108 ), enhancing job satisfaction and reducing burnout ( 105 , 109 ). Therefore, nursing educators should guide students in seeking meaningful experiences and learning opportunities to build self-efficacy, improve adaptability, and reduce the incidence of compassion fatigue. Abusubhiah et al. (2023) advocate for the use of multimodal interventions, including flipped classrooms, simulations, debriefing, and role-playing, to better enhance nursing students’ self-efficacy ( 110 ). Development and application of a machine learning-based predictive model for compassion fatigue among nursing students during internships To our knowledge, this study is the first to develop a user-friendly, personalized model to predict compassion fatigue among nursing interns. Utilizing machine learning techniques, we confirmed the accuracy of this model, which contributes to optimizing clinical management. Additionally, our study identified nine independent risk factors for compassion fatigue: gender, specialty, frequency of night shifts, career intention, resilience scores, professional identity scores, social support scores, coping style scores, and self-efficacy scores. Recently, a study based on 21 tertiary hospitals in China developed a risk prediction model for compassion fatigue among emergency department nurses using a logistic regression model ( 111 ). This model included only seven factors: job position, job satisfaction, dietary habits, daily sleep duration, occupational stress, physical harassment, and workplace violence levels. In contrast, our model demonstrated higher predictive efficiency, covering a broader range of influencing factors, and providing a more comprehensive assessment of the risk of compassion fatigue. However, our model has not yet been validated in external populations, so its external validity may need further confirmation. Our prediction model can identify nursing students at high risk of compassion fatigue before symptoms manifest. In clinical applications, the online measurement tool can be combined with existing compassion fatigue scales to provide a comprehensive assessment of nursing students. For instance, a student currently showing no symptoms of compassion fatigue may score zero on the scale, but this does not mean they will not experience compassion fatigue in the future. Our online tool calculates the probability of each student developing compassion fatigue, serving as a powerful supplement to existing scales and assisting nursing educators and clinical managers in devising appropriate interventions to support students' health and professional development. Strengths and limitation This study has yielded some promising findings, primarily due to the following advantages. First, there are few existing machine learning prediction models specifically addressing compassion fatigue in nursing students during the internship, and our study fills this gap. Second, this study innovatively combines LPA and machine learning to identify different risk groups among nursing students through LPA and to predict individual risk levels using machine learning models. The application of machine learning algorithms enhances the predictive power of the model, offering advantages over traditional risk prediction methods, and aids school and hospital administrators in identifying high-risk individuals for compassion fatigue and implementing targeted interventions. However, this study also acknowledges several limitations. First, the cross-sectional design may limit causal inference and introduce selection bias. Second, the relatively small sample size and lack of external validation may affect the robustness of the model. Therefore, further prospective studies with larger and more diverse samples are needed to evaluate the diagnostic sensitivity and specificity of the model. Third, the selection of input data and model parameters may vary when encountering new data, necessitating further research. Finally, the routine collection of psychosocial parameters in clinical settings may be challenging, potentially hindering the practical application of the model. Therefore, developing predictive models that include only objective parameters with high predictive performance would be a beneficial approach. Conclusion This study developed a predictive model integrating demographic characteristics, work-related, and psychosocial variables, demonstrating excellent calibration and predictive capability for compassion fatigue among nursing students. The resulting online tool can screen students at moderate to high risk during internships, aiding nursing managers in optimizing prevention strategies and reducing incidence. Abbreviations LPA, latent profile analysis LMR, Lo-Mendell-Rubin likelihood ratio test BLRT, bootstrap likelihood ratio test AIC, Akaike Information Criterion BIC, Bayesian Information Criterion aBIC, adjusted Bayesian Information Criterion LASSO, the Least Absolute Shrinkage and Selection Operator AUC, area under the receiver operating characteristic curve ROC, receiver operating characteristic DCA, Decision curve analysis Declarations Ethics approval and consent to participate Institutional Review Board approval was granted for this study by the institution review board of Hunan Traditional Chinese Medical College, with an approval number of YX202212001. Informed consent was obtained from all participants included in the study. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author[LJY] on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The study was funded by 2024 Hunan Provincial Universities Ideological and Political Work Quality Project (Practice Education)[ grant number:24JP039]. Authors’ contributions LIY and XT conceived and designed the study. LJY, TS, and XT contributed to the acquisition, analysis, and interpretation of data. LJY, YL, JJZ, and MFJH were involved in investigation, methodology, and data curation. LJY drafted the manuscript. TX revised subsequent drafts. All authors reviewed and approved the final manuscript. Acknowledgements Thank you to all nursing interns who volunteered to participate in this study and also to the full-time student counselors for their great support in data collection. 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BMC Nurs. 2023;22(1):459. Zhou Y, Guo X, Yin H. A structural equation model of the relationship among occupational stress, coping styles, and mental health of pediatric nurses in China: a cross-sectional study. BMC Psychiatry. 2022;22(1):416. Visier-Alfonso ME, Sarabia-Cobo C, Cobo-Cuenca AI, Nieto-López M, López-Honrubia R, Bartolomé-Gutiérrez R, et al. Stress, mental health, and protective factors in nursing students: An observational study. Nurse Educ Today. 2024;139:106258. Ding Y, Yang Y, Yang X, Zhang T, Qiu X, He X, et al. The Mediating Role of Coping Style in the Relationship between Psychological Capital and Burnout among Chinese Nurses. PLoS ONE. 2015;10(4):e0122128. Zhang J, Wang X, Xu T, Li J, Li H, Wu Y, et al. The effect of resilience and self-efficacy on nurses' compassion fatigue: A cross-sectional study. J Adv Nurs. 2022;78(7):2030–41. Hao X, Chen X, Sun C, Zhu Z, Qiao C, Ji M. The mediating effect of general self-efficacy of outpatient nurses on the relationship between perception of work environment and compassion fatigue. Chin Nurs Manage. 2023;23(3):461–6. Fangonil-Gagalang E. Association of self-efficacy and faculty support on students' readiness for practice. J Prof Nurs. 2024;52:30–9. Liu Y, Chong MC, Han Y, Wang H, Xiong L. The mediating effects of self-efficacy and study engagement on the relationship between specialty identity and career maturity of Chinese nursing students: a cross-sectional study. BMC Nurs. 2024;23(1):339. Xu R, Du W, Liu Z. Correlation analysis of general self-efficacy and nursing quality among neurology nurses in Lanzhou. Chin J Practical Nurs. 2016;32(19):1445–50. Abusubhiah M, Walshe N, Creedon R, Noonan B, Hegarty J. Self-efficacy in the context of nursing education and transition to practice as a registered practitioner: A systematic review. Nurs Open. 2023;10(10):6650–67. Xie W, Liu M, Okoli CTC, Zeng L, Huang S, Ye X, et al. Construction and evaluation of a predictive model for compassion fatigue among emergency department nurses: A cross-sectional study. Int J Nurs Stud. 2023;148:104613. Additional Declarations No competing interests reported. Supplementary Files Authorchecklist.doc STROBEchecklist.docx Cite Share Download PDF Status: Published Journal Publication published 19 Dec, 2024 Read the published version in BMC Medical Education → Version 1 posted Editorial decision: Revision requested 09 Sep, 2024 Reviews received at journal 03 Sep, 2024 Reviewers agreed at journal 23 Aug, 2024 Reviews received at journal 08 Aug, 2024 Reviewers agreed at journal 07 Aug, 2024 Reviewers invited by journal 05 Aug, 2024 Editor invited by journal 16 Jul, 2024 Editor assigned by journal 15 Jul, 2024 Submission checks completed at journal 15 Jul, 2024 First submitted to journal 09 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4709842","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":337416672,"identity":"3e44d068-ca42-44a5-a597-9443f69fd69e","order_by":0,"name":"Lijuan Yi","email":"","orcid":"","institution":"Hunan Traditional Chinese Medical College","correspondingAuthor":false,"prefix":"","firstName":"Lijuan","middleName":"","lastName":"Yi","suffix":""},{"id":337416675,"identity":"8ef5a933-965c-4a0f-a488-641922ab0c3f","order_by":1,"name":"Ting Shuai","email":"","orcid":"","institution":"Peking University School and Hospital of Stomatology","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Shuai","suffix":""},{"id":337416676,"identity":"93071ede-4462-466a-ae2f-65d08064a88e","order_by":2,"name":"Yi Liu","email":"","orcid":"","institution":"Hunan Traditional Chinese Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Liu","suffix":""},{"id":337416677,"identity":"a823a14b-a579-4a70-8728-9480cc57c61d","order_by":3,"name":"Jingjing Zhou","email":"","orcid":"","institution":"Hunan Traditional Chinese Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Zhou","suffix":""},{"id":337416678,"identity":"c3c30dc1-d805-4dad-8d53-452e3810d72e","order_by":4,"name":"Maria F. Jiménez-Herrera","email":"","orcid":"","institution":"Rovira i Virgili University","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"F.","lastName":"Jiménez-Herrera","suffix":""},{"id":337416679,"identity":"b8a3569f-494f-48d4-8be2-73b90d322db0","order_by":5,"name":"Xu Tian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYLCCBAYGOQZ2EMuAOA2MDUAtxgzMKFoSCGgBEokNzOgW4wIGNxLYHzyouZPe38xjurmggCFx+4wExg8/f+DWIglU0JBw7FnujMM8ZrdnGDAkzrmRwCzZg8cWfgmQFrbDuQ0gLTxALTN4DrAx8ODRwgbW8u9wujyyFsY/hGxJbDucYADXwt7AxozPFsmeh40zEvsOG248zFYG1CJhPIO9sVlaJg23FoPjyQc+/vh2WF7uePO22zx/bGRnMDMf/PjGBrcWaLTAgQSGyCgYBaNgFIwCMgAA+1dL+fGseW8AAAAASUVORK5CYII=","orcid":"","institution":"Chongqing Traditional Chinese Medicine Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xu","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2024-07-09 07:09:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4709842/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4709842/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12909-024-06505-9","type":"published","date":"2024-12-19T15:58:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62013457,"identity":"7d0286e5-4ebb-49d3-89b1-51ab07f275a8","added_by":"auto","created_at":"2024-08-08 08:19:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":81456,"visible":true,"origin":"","legend":"\u003cp\u003eScree plot of change trend of adjusted Bayesian Information Criterion (aBIC).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4709842/v1/9810a4d9f706777bd4a77f6d.png"},{"id":62014376,"identity":"24df71d8-ffa6-4c6b-950f-250823fb53ac","added_by":"auto","created_at":"2024-08-08 08:27:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":159953,"visible":true,"origin":"","legend":"\u003cp\u003eThree classes of the best-fitting 3-class model based on Compassion Fatigue Short Scale (CFSS).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4709842/v1/44f8717af78e669d076cc554.png"},{"id":62013459,"identity":"f6f64419-9a1a-4100-b0a8-3a6cd8a356d7","added_by":"auto","created_at":"2024-08-08 08:19:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":171015,"visible":true,"origin":"","legend":"\u003cp\u003eFeature selection based on the LASSO regression. Coefficient profile (A) and penalty plot (B).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4709842/v1/09fe11d4eeed95002da73ef8.png"},{"id":62013464,"identity":"32a19021-c5f9-4c7d-8df5-8daccac82658","added_by":"auto","created_at":"2024-08-08 08:19:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":468152,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and comparison of eight ML models. ROC curves of eight ML models for predicting compassion fatigue in the training (A) and validation(B) sets, calibration curves (C) and forest plot of various models (D).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4709842/v1/da509ab955676997ab080cd0.png"},{"id":62014380,"identity":"392b08f7-e64e-4a55-af80-5db66e51f15b","added_by":"auto","created_at":"2024-08-08 08:27:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":321569,"visible":true,"origin":"","legend":"\u003cp\u003eDevelopment and evaluation of XGBoost classification model. ROC curves of XGBoost using the 5-fold cross-validation in the training (A), validation (B), and test (C) sets, respectively.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4709842/v1/7c07db1214bc464698501da8.png"},{"id":62014382,"identity":"8d5e90f5-5a77-4cd9-b0ef-023fa2acca73","added_by":"auto","created_at":"2024-08-08 08:27:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":330400,"visible":true,"origin":"","legend":"\u003cp\u003eInterpretation of the results of XGBoost classification model based on the SHAP method. SHAP summary chart (A) and bar chat (B).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4709842/v1/0bf616ef355487b45e10e8ea.png"},{"id":62013462,"identity":"f41708e0-0276-4887-be16-b99976ac2c94","added_by":"auto","created_at":"2024-08-08 08:19:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":232561,"visible":true,"origin":"","legend":"\u003cp\u003eOnline prediction model of compassion fatigue.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4709842/v1/31ba6d0b37b378af6264d43f.png"},{"id":72202577,"identity":"b39a7894-f667-4a68-a4ed-f3e21506871e","added_by":"auto","created_at":"2024-12-23 16:14:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2715144,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4709842/v1/8b77d13d-3881-4694-91ad-dc1ce25249c1.pdf"},{"id":62015170,"identity":"9614463c-3172-4ce1-b4d0-f4ff2679d992","added_by":"auto","created_at":"2024-08-08 08:35:17","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":78848,"visible":true,"origin":"","legend":"","description":"","filename":"Authorchecklist.doc","url":"https://assets-eu.researchsquare.com/files/rs-4709842/v1/ed2105f9599cb214319ffbbd.doc"},{"id":62014379,"identity":"03679c13-abf9-488b-9226-5a2baed8a24c","added_by":"auto","created_at":"2024-08-08 08:27:17","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":31792,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-4709842/v1/bc21219e60bf44b241aa3f72.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a machine learning-based predictive model for compassion fatigue in nursing interns: A cross-sectional study with latent profile analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eNursing is a profession defined by its commitment to compassion, but it is also accompanied by significant stress. Nurses frequently witness patients\u0026rsquo; suffering and distress while providing care and support. Prolonged exposure to such traumatic experiences can result in psychological distress, impairing their capacity to deliver optimal care (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This phenomenon, known as compassion fatigue, was first defined by Joinson in 1992, and it encompasses physical, psychological, and social dysfunction arising from continuous exposure to patient suffering or traumatic events (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Compassion fatigue comprises two elements: burnout and secondary traumatic stress (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). This emotional and psychological exhaustion leads to various health issues among nurses, including musculoskeletal disorders, sleep disturbances, depression, and reduced professional identity and work engagement (\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCompassion fatigue is not exclusive to registered nurses; it also affects nursing students (\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In China, pre-licensure nursing education includes both associate (three or five-year programs) and baccalaureate (four-year programs) tracks. These programs emphasize a combination of theoretical knowledge and practical skills. After completing on-campus theoretical studies, students are required to undertake at least eight months of clinical placement in secondary or higher-level hospitals. During this clinical placement period, these students are referred to as nursing interns. A survey of 972 Chinese nursing interns revealed that 97.8% experienced moderate burnout, and 55.3% suffered from secondary traumatic stress (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). High levels of compassion fatigue or burnout are associated with a higher intention to drop out, increasing actual attrition rates (\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Additionally, burnout developed during educational programs may persist after graduation, impacting the health and retention of new nurses (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Thus, compassion fatigue or burnout adversely affects the career development of nursing students. Early identification and intervention for those at risk of compassion fatigue are crucial to prevent the worsening of symptoms.\u003c/p\u003e \u003cp\u003eCurrent assessments of compassion fatigue primarily rely on surveys (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Researchers such as Figley and Stamm have developed several measurement tools, including the Compassion Fatigue Self-Test (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), the Compassion Satisfaction and Fatigue Scale (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), the Compassion Fatigue Scale (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), the Compassion Fatigue Short Scale (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) and the Professional Quality of Life Scale (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). While these tools are essential for assessing compassion fatigue, they have limitations. For instance, some tools consider compassion fatigue as a combination of burnout and secondary traumatic stress or focus on a single dimension, complicating the assessment process (\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Additionally, the Compassion Fatigue Short Scale, despite providing a straightforward measurement method, lacks clear cutoff values, making it difficult to distinguish between low-risk and high-risk individuals (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). More importantly, the aforementioned tools typically assess compassion fatigue among nursing interns using total scores, which overlooks individual differences and are primarily designed for assessment rather than prediction(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Consequently, there is an urgent need for a new method to effectively assess and predict the risk of compassion fatigue among nursing interns.\u003c/p\u003e \u003cp\u003eSeveral psychosocial factors, such as social support, coping strategies, self-efficacy, psychological resilience, and professional identity, are closely related to compassion fatigue among nursing students (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Studies have shown that educational background and career-related factors, such as academic major, program length, previous student leadership, and career intentions, are significantly associated with compassion fatigue or burnout risk (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Demographic factors like gender, residence, only-child status, and monthly expenses, as well as internship-related characteristics such as the level of the internship hospital and the frequency of night shifts, are also associated with compassion fatigue or burnout (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). These factors should be considered when assessing nursing students at potential risk.\u003c/p\u003e \u003cp\u003eCompassion fatigue is a complex and dynamic phenomenon. Traditional \u0026ldquo;one-size-fits-all\u0026rdquo; approaches are insufficient to convey its impact on different psychological profiles. Existing cross-sectional surveys on compassion fatigue among nursing students typically use variable-centered approaches, measuring compassion fatigue through total scores from scales or subscales, neglecting within-group variability (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). To reveal the heterogeneity of compassion fatigue among nursing students more comprehensively and precisely and to identify characteristics of different profiles, combining latent profile analysis (LPA) with machine learning could be a potential solution.\u003c/p\u003e \u003cp\u003eLPA is a person-centered statistical method that classifies nursing students into different risk groups based on the measurement data of compassion fatigue, providing a basis for tailored interventions (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Unlike traditional cutoff-based methods, LPA identifies unobserved heterogeneity within the population based on individuals\u0026rsquo; responses to continuous variables, grouping those with similar response patterns into homogeneous subgroups (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). This approach addresses the issue of neglecting within-group variability when using total scores, making LPA a superior choice (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). However, LPA lacks predictive capabilities, necessitating its combination with other methods for predictive functions. Machine learning, a technique for learning patterns from data and making predictions, possesses strong data processing and prediction capabilities (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). In compassion fatigue research, machine learning can construct predictive models based on LPA classification results and other related variables, enabling accurate predictions of individual risk levels. Therefore, combining LPA with machine learning can provide a more comprehensive assessment and prediction of compassion fatigue risk among nursing interns.\u003c/p\u003e \u003cp\u003eThe main objectives of this study are: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to identify potential classifications of compassion fatigue among nursing interns using LPA; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) to develop and validate machine learning models for predicting individual risk levels; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) to develop an online prediction tool for practical application.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted from December 2021 to June 2022 among nursing interns at 10 public junior colleges in Hunan Province, China. This design was chosen to provide a comprehensive overview of the population at a specific time point. The study adheres to guidelines outlined in the STROBE and TRIPOD statements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eParticipants were recruited using a convenience sampling method. Inclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) enrollment in a three- or five-year associate nursing program, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) active participation in a clinical internship lasting at least eight months in a secondary-level or above hospital, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) willingness to participate with informed consent. Nursing interns whose clinical practice was in clerical management or administration, without direct patient contact, were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample size\u003c/h2\u003e \u003cp\u003eLarger sample sizes are typically used in survey research to achieve more accurate and stable results (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). However, there is no standardized method for calculating sample size in survey studies using machine learning techniques (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). For LPA, a sample size of more than 500 cases is recommended to ensure accuracy (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Furthermore, a review by Spurk et al. revealed that 53.4% of studies used sample sizes greater than 500, further supporting the appropriateness of this rule of thumb (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eFull-time student counselors responsible for managing nursing students during their internships, were trained as research assistants. They provided standardized explanations of the purpose, risks, and benefits of this study. Data were collected via online surveys created on the WenJuanXing platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wjx.cn\u003c/span\u003e\u003cspan address=\"https://www.wjx.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), shared through WeChat groups. Each IP address was restricted to one submission. To reduce missing data and ensure data quality, the online survey required all questions to be answered. Participants\u0026rsquo; anonymity and voluntary participation were emphasized, and participants could withdraw at any time. Surveys with identical or patterned responses were excluded. After removing 149 invalid responses, the final sample consisted of 2256 participants, resulting in an effective response rate of 93.8%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eInstruments\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eGeneral demographic information collection table\u003c/h2\u003e \u003cp\u003eA self-designed table collected sociodemographic data including gender, academic major, program length, place of residence, only-child status, monthly expenditure, previous experience as a student leader, hospital level during internship, number of night shifts per month, and career intentions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eThe Compassion Fatigue Short Scale\u003c/h2\u003e \u003cp\u003eThe Compassion Fatigue Short Scale, developed by Adams et al., comprises 13 items measuring secondary traumatic stress (5 items) and job burnout (8 items) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Responses range from 1 (never) to 10 (very often), yielding a total score ranging from 13 to 130, where higher scores indicate greater levels of compassion fatigue. The Chinese version, translated and validated by Sun (2015) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), demonstrated good reliability and validity, with Cronbach\u0026rsquo;s alpha coefficients ranging from 0.87 to 0.95. In this study, the overall Cronbach\u0026rsquo;s alpha coefficient of the Chinese version was 0.92.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eThe Professional Identity Scale\u003c/h2\u003e \u003cp\u003eProfessional identity was measured using the Professional Identity Scale by Brown et al., consisting of 10 items rated on a 5-point scale from 1 (never) to 5 (always) (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Higher scores indicate stronger professional identity. The Chinese version, translated and validated by Lu et al., achieved a Cronbach's alpha coefficient of 0.82 (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). In this study, the overall Cronbach's alpha coefficient of the Chinese version was 0.80.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe General Self-efficacy Scale\u003c/h2\u003e \u003cp\u003eThe General Self-Efficacy Scale, developed by Schwarzer and Jerusalem (1995) (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) and adapted for Chinese population by Zeng et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), includes 10 items rated from 1 (not true at all) to 4 (exactly true), with total scores ranging from 10 to 40, where higher scores indicate greater self-efficacy. Zeng et al.'s (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) research demonstrated that the scale exhibits good internal consistency and criterion validity, with an overall Cronbach's alpha coefficient of 0.89 in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe Perceived Social Support Scale\u003c/h2\u003e \u003cp\u003eThe Perceived Social Support Scale, developed by Zimet et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), translated by Jiang (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e) and modified by Yan et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), includes 12 items across three dimensions: family support (items 3, 4, 8, 11), friend support (items 6, 7, 9, 12), and other support (items 1, 2, 5, 10). Responses range from 1 (strongly disagree) to 7 (strongly agree), resulting in a total score ranging from 12 to 84. Higher scores indicate higher levels of perceived social support. In Yan et al.'s study, the scale demonstrated internal consistency and test-retest reliability coefficients of 0.87 and 0.85, respectively (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). In the present study, the Chinese version of the scale achieved an overall Cronbach\u0026rsquo;s alpha coefficient of 0.92.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe 10-item Connor-Davidson Resilience Scale\u003c/h2\u003e \u003cp\u003eThe 10-item Connor-Davidson Resilience Scale is a refinement by Campbell-Sills of the original scale developed by Connor and Davidson (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). This unidimensional scale comprises 10 items, each rated on a 5-point scale (0\u0026ndash;4), with higher scores indicating greater resilience. In this study, we utilized the Chinese version of the scale, translated and validated by Ye in 2016 (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). In a sample of Chinese nursing students, this scale accounted for 48.641% of the total variance, with a Cronbach's α coefficient of 0.851, demonstrating good reliability and validity (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). In the present study, the overall Cronbach's α coefficient for the scale was 0.93.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSimple Coping Style Questionnaire\u003c/h2\u003e \u003cp\u003eThe simple coping style questionnaire was adapted and translated by Xie et al. based on Folkman and Lazarus' Ways of Coping Questionnaire (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). The 20-item scale comprises two dimensions: positive coping styles (items 1\u0026ndash;12) and negative coping styles (items 13\u0026ndash;20). Each item uses a Likert four-point scale, ranging from 1 (do not take) to 4 (often take). Higher scores in the positive coping style dimension indicate a greater likelihood of adopting positive coping strategies, while higher scores in the negative coping style dimension indicate a greater likelihood of adopting negative coping strategies. The scale has demonstrated good reliability and validity among Chinese populations, with a Cronbach\u0026rsquo;s α coefficient of 0.90 (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). In the present study, the scale achieved an overall Cronbach\u0026rsquo;s α coefficient of 0.87.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eDescriptive and univariate analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics summarized the general characteristics of the participants. Continuous variables, which did not follow a normal distribution, were represented as median and inter-quartile range, while categorical variables were shown as numbers and percentages. Univariate analysis, using the Mann-Whitney U test, chi-squared test, or Fisher\u0026rsquo;s exact test as appropriate, identified predictive factors for compassion fatigue or burnout. The candidate factors considered included demographic characteristics, professional identity, self-efficacy, social support, psychological resilience, and coping styles. Analyses were performed using SPSS 26.0 (IBM Corp., Armonk, NY, USA), with a significance threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for two-sided test.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLatent profile analysis\u003c/h2\u003e \u003cp\u003eLPA was conducted using the 13 items from the Compassion Fatigue Short Scale as indicators, employing robust maximum likelihood estimation to identify subgroups of compassion fatigue symptoms. Various models were compared based on entropy, the Lo-Mendell-Rubin likelihood ratio test (LMR), the bootstrap likelihood ratio test (BLRT), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and adjusted Bayesian Information Criterion (aBIC) (\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Specifically, an entropy value\u0026thinsp;\u0026gt;\u0026thinsp;0.80 indicated that the latent classes were highly discriminative, and significant p-values for the LMR and BLRT suggested that the k-class model was preferable to the k-1 class model (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). Additionally, lower values of AIC, BIC, and aBIC indicated better model fit, while the \u0026ldquo;turning point\u0026rdquo; in the scree plot for aBIC suggested the appropriate number of classes (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). Ultimately, the number of latent profiles was determined by a combination of these fit criteria. Analyses were conducted using Mplus version 8.3 (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo facilitate the construction of the subsequent predictive model, following recommendation from previous studies (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e), we assigned individuals who belonged to the latent profile representing the lowest level of symptoms or risks as \u0026ldquo;non-cases,\u0026rdquo; while other individuals were considered \u0026ldquo;cases.\u0026rdquo;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFeatures selection\u003c/h2\u003e \u003cp\u003eAfter determining the optimal model and defining classifications, we assessed differences in some sociodemographic characteristics, professional identity, self-efficacy, social support, psychological resilience, and coping styles between the \u0026ldquo;non-cases\u0026rdquo; and \u0026ldquo;cases\u0026rdquo; groups. Significant variables from the univariate analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) were further analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression, a technique that penalizes regression coefficients to optimize the model by retaining only significant predictors(\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). This approach handles complex covariance structures and improves predictive accuracy. LASSO regression was performed using Lasso CV with 10-fold cross-validation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment and evaluation of machine learning models learning\u003c/h2\u003e \u003cp\u003eEight machine learning algorithms were used to develop and compare models for predicting the risk of compassion fatigue, including logistic regression, support vector machine, random forest, multi-layer perceptron, extreme gradient boosting (XGBoost), gradient boosting decision trees, Gaussian naive Bayes, and adaptive boosting. All models were implemented using Python 3.7, with the \"xgboost 2.0.1\" package for XGBoost and the \"scikit-learn 1.1.3\" package for the remaining algorithms. Models were trained and validated using bootstrap resampling, with a 7:3 training-to-testing ratio. Performance was evaluated using metrics such as area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Calibration was assessed by comparing predicted and observed incidence of \u0026ldquo;cases.\u0026rdquo;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eModel optimization and evaluation\u003c/h2\u003e \u003cp\u003eTo ensure robustness and mitigate overfitting, 10-fold cross-validation assessed predictive performance. The dataset was randomly divided into ten equal parts, with training and validation repeated five times. Model discrimination was evaluated using receiver operating characteristic (ROC) analysis and quantified by AUC. Calibration plots assessed the agreement between predicted probabilities and actual outcomes. Decision curve analysis (DCA) estimated the clinical utility and net benefit. Feature importance was assessed using SHAP analysis, with higher absolute SHAP values indicating greater impact on predictions (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). Additionally, we explored the distribution of feature values and their relationship with model predictions to gain further insights into model behavior. SHAP analysis was conducted using the \u0026ldquo;shap 0.43.0\u0026rdquo; package.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eData were collected from 2256 nursing interns. Most participants were female (2077, 92.07%), came from rural areas (1784, 79.08%), and were non-only children (1962, 86.97%). Regarding economic status, 69.77% had a monthly expenditure between 1000 and 2000 RMB (1574, 69.77%). A significant majority were enrolled in nursing programs (1660, 73.58%), with most of them in three-year programs (1736, 76.95%). Approximately half had experience serving as student leaders (1172, 51.95%).\u003c/p\u003e \u003cp\u003eIn terms of internship conditions, 75.71% interned at tertiary hospitals (1708, 75.71%), and most worked night shifts 3 to 4 times per month (1037, 45.96%). Career intention surveys showed that 86.13% intended to pursue a nursing career (1943, 86.13%). Psychological assessments revealed median scores of 39.00 for professional identity, 2.800 for self-efficacy, 58.00 for social support, 23.00 for psychological resilience, and 34.00 for coping style. Further details of participants\u0026rsquo; characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eUnivariate analysis of influencing factors of compassion fatigue of nursing interns\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-case \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCase \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic major, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNursing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1660 (73.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e973 (76.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e687 (69.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMidwifery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e596 (26.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e294 (23.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e302 (30.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of schooling, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3-year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1736 (76.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e963 (76.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e773 (78.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5-year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e520 (23.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304 (23.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e216 (21.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179 (7.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125 (9.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54 (5.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2077 (92.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1142 (90.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e935 (94.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e472 (20.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e269 (21.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e203 (20.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1784 (79.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e998 (78.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e786 (79.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnly child or not, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e294 (13.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e173 (13.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e121 (12.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1962 (86.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1094 (86.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e868 (87.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly expenses, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;1000 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e420 (18.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e242 (19.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e178 (18.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000\u0026ndash;2000 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1574 (69.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e879 (69.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e695 (70.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2001\u0026ndash;3000 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e262 (11.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e146 (11.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116 (11.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhether as a student cadre, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1172 (51.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e656 (51.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e516 (52.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1084 (48.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e611 (48.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e473 (47.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital level during internship, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1708 (75.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e985 (77.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e723 (73.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e548 (24.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e282 (22.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e266 (26.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of night-shift per month, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e803 (35.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e468 (36.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e335 (33.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1037 (45.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e590 (46.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e447 (45.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e278 (12.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e145 (11.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e133 (13.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e138 (6.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64 (5.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74 (7.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCareer intention, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1943 (86.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1164 (91.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e779 (78.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e313 (13.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103 (8.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e210 (21.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional identity, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.00 (34.00, 43.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.00 (37.00, 44.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.00 (31.00, 40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-efficacy, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.800 (2.50, 3.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.80 (2.60, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.70 (2.40, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial support, median IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.00 (50.00, 66.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.00 (53.00, 69.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.00 (48.00, 61.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological resilience, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.00 (20.00, 29.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.00 (21.00, 30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.00 (18.00, 25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoping style, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.00 (27.00,4 0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.00 (27.00, 40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.00 (27.00, 39.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote\u003c/em\u003e: IQR, Interquartile spacing; CF, compassion fatigue. a:\"non-cases\" refers to nursing interns with low levels of compassion fatigue; b: \"cases\" refers to nursing interns with moderate to severe levels of compassion fatigue.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eLatent profiles of nursing students\u0026rsquo; compassion fatigue\u003c/h2\u003e \u003cp\u003eLPA was performed with one to four latent classes, and the fit indices for these models are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. All classifications had entropy values exceeding 0.9. The BLRT was statistically significant across all models, while the LMR was significant only for the one to three-class models. With an increase in the number of classes, the AIC, BIC, and aBIC values decreased, and the scree plot of aBIC flattened after the three-class model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Considering statistical criteria, interpretability, and parsimony, the three-class model was selected as the optimal model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGoodness of Fit Statistics for 1 to 4 Class Models \u003cem\u003eNote\u003c/em\u003e: AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; aBIC, adjusted Bayesian Information Criterion; LMR, Lo\u0026ndash;Mendell\u0026ndash;Rubin; BLRT, bootstrap likelihood ratio test; n.a., not applicable.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEntropy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLMR(p)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBLRT(p)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSample proportion (%) per class\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135405.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135553.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e135471.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125201.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125430.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e125303.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e71.37/28.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122468.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122777.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122605.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55.73/32.17/12.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121328.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121717.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e121501.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.67/55.70/8.23/10.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the average latent class probabilities for the most likely latent class membership in the three-class model (0.968, 0.940, and 0.960), indicating reasonable classification and good distinction. The distribution and conditional means of the Compassion Fatigue Short Scale items for each class in the three-class model are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Based on the conditional means of the items for each class, Class 1 (n\u0026thinsp;=\u0026thinsp;1257, 55.73%) was defined as the \u0026ldquo;low compassion fatigue\u0026rdquo; group, Class 2 (n\u0026thinsp;=\u0026thinsp;726, 32.17%) as the \u0026ldquo;moderate compassion fatigue\u0026rdquo; group, and Class 3 (n\u0026thinsp;=\u0026thinsp;273, 12.10%) as the \u0026ldquo;severe compassion fatigue\u0026rdquo; group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage latent class probabilities for most likely latent class membership by latent class\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLatent class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eLatent class membership\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eInfluencing factors of nursing students\u0026rsquo; compassion fatigue\u003c/h2\u003e \u003cp\u003eTo explore influencing factors and develop a risk prediction model for compassion fatigue among nursing interns, participants in the \u0026ldquo;low compassion fatigue\u0026rdquo; group identified by LPA were categorized as \u0026ldquo;non-cases\u0026rdquo; (i.e., without compassion fatigue), and those in the \u0026ldquo;moderate compassion fatigue\u0026rdquo; and \u0026ldquo;severe compassion fatigue\u0026rdquo; groups were categorized as \u0026ldquo;cases\u0026rdquo; (i.e., potentially experiencing compassion fatigue). Univariate analysis revealed statistically significant differences between the \u0026ldquo;non-case\u0026rdquo; and \u0026ldquo;case\u0026rdquo; groups in terms of major (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;15.358, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), gender (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;14.759, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), level of internship hospital (χ2\u0026thinsp;=\u0026thinsp;6.498, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), frequency of night shifts per month (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;8.868, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031), career intention (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;79.820, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), professional identity (z\u0026thinsp;=\u0026thinsp;17.774, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), self-efficacy (z\u0026thinsp;=\u0026thinsp;10.160, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), social support (z\u0026thinsp;=\u0026thinsp;13.058, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), psychological resilience (z\u0026thinsp;=\u0026thinsp;16.793, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and coping style (z\u0026thinsp;=\u0026thinsp;2.691, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These 10 variables were included in the LASSO model. Using 10-fold cross-validation, we identified 9 key features for developing the prediction model: major, gender, frequency of night shifts per month, career intention, professional identity, self-efficacy, social support, psychological resilience, and coping style (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eComparison of multiple classification models\u003c/h2\u003e \u003cp\u003eWe compared the performance of eight machine learning classification models for predicting the risk of compassion fatigue among nursing students during their internship. The XGBoost model demonstrated high stability and accuracy in both the training and validation sets, outperforming others with its AUC, accuracy, and F1 scores. In contrast, the Random Forest model showed perfect performance in the training set but significant overfitting, resulting in a notable decline in the validation set. Other models, such as Logistic Regression, Support Vector Machine, and Multi-Layer Perceptron, showed relatively weaker performance across various metrics. Results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides a comprehensive visualization of the ROC curves for all models in the training and validation sets (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), the calibration plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), and the forest plot with the AUC score results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictive performance of the eight machine learning techniques in the training and validation sets for compassion fatigue of nursing interns\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eF1score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eTraining set\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXGBoost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdaBoost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eValidation set\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXGBoost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdaBoost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBDT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: AUC, area under the curve; PPV, positive predictive value; NPV, negative predictive value; LR, logistic regression; SVM, support vector machine; RF, random forest; GBDT, gradient boosting decision tree; MLP, multi-layer perceptron; XGBoost, extreme gradient boosting; GNB, Gaussian naive Bayes; AdaBoost, adaptive boosting.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eModel optimization\u003c/h2\u003e \u003cp\u003eThe XGBoost model, identified as the best performer in predicting compassion fatigue risk, was further optimized through hyper-parameter tuning and selected 9 pre-identified variables as input features. A 10-fold cross-validation strategy was employed, dividing participants into 10 groups, with each group serving as the test set in turn, while the remaining 9 groups were used for training and validation. Figures\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C display the ROC curves and AUC values of the XGBoost model in the training, validation, and test sets, which were 0.840, 0.768, and 0.731, respectively, indicating good predictive capability (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C). Before evaluating the model's accuracy, we generated a learning curve. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, the error between the training set and the validation set gradually converged as the training samples increased, indicating no significant overfitting. We then assessed the model's accuracy using a calibration plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE), which demonstrated that the predicted probabilities of the XGBoost model were highly consistent with the actual incidence of \"cases.\" Finally, decision curve analysis (DCA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF) revealed that the model provided significant net benefits within a risk threshold below 70% compared to the \u0026ldquo;treat all\u0026rdquo; or \u0026ldquo;treat none\u0026rdquo; strategies, further demonstrating its effectiveness and practicality in real-world applications.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance of the XGBoost classification model for predicting the risk of compassion fatigue of nursing interns\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eF1 score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraining set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValidation set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest set\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: AUC, area under the curve; PPV, positive predictive value; NPV, negative predictive value. XGBoost, extreme gradient boosting.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eModel interpretation\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the impact of various features on the prediction outcomes of the model. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, the SHAP summary plot, indicates that psychological resilience, professional identity, and social support are the features with the greatest influence on the model\u0026rsquo;s output. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, the bar chart of mean absolute SHAP values, further confirms this finding. Figures\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD decompose the feature contributions for two sample students, demonstrating the individual-level impact of these features on risk prediction. These figures clearly identify which feature values significantly affect the final risk prediction scores and cause them to fluctuate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of a web-based application for predicting compassion fatigue\u003c/h2\u003e \u003cp\u003eWe developed a web-based application based on the final model\u0026rsquo;s predicted risks (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.xsmartanalysis.com/model/list/predict/model/html?mid=\u003c/span\u003e\u003cspan address=\"http://www.xsmartanalysis.com/model/list/predict/model/html?mid=\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e15239\u0026amp;symbol\u0026thinsp;=\u0026thinsp;21Dx71Ab62wF580Bl642\u003c/span\u003e), facilitating accurate individual risk assessment (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003eLatent profile analysis of compassion fatigue among nursing students during the internship\u003c/h2\u003e \u003cp\u003eThis study utilized LPA to categorize compassion fatigue levels among nursing interns. Based on model fit criteria, the optimal model identified three profiles: \u0026ldquo;low compassion fatigue,\u0026rdquo; \u0026ldquo;moderate compassion fatigue,\u0026rdquo; and \u0026ldquo;severe compassion fatigue.\u0026rdquo;\u003c/p\u003e \u003cp\u003eThe \u0026ldquo;low compassion fatigue\u0026rdquo; group, comprising 55.73% of the participants, had average scores of 18.74 for burnout and 10.14 for secondary traumatic stress, with an overall average score of 28.874 on the Compassion Fatigue Short Scale. Although these nursing interns showed low overall compassion fatigue, they scored higher on item 2 (\u0026ldquo;I feel I have not accomplished much in my life\u0026rdquo;) and item 7 (\u0026ldquo;I often feel weak, tired, or exhausted as a caregiver\u0026rdquo;). These scores reflect their emotional and psychological adaptation to new environments and work challenges, likely due to the high intensity of internship tasks, academic pressure, and the fear of incompetence during the transition to their new roles (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). Despite the overall low level of compassion fatigue, the higher scores on specific items warrant attention to prevent progression to moderate or severe levels of fatigue.\u003c/p\u003e \u003cp\u003eThe \u0026ldquo;moderate compassion fatigue\u0026rdquo; group included 32.17% of the participants, with average scores of 37.78 for burnout and 19.14 for secondary traumatic stress, totaling 56.92 on the Compassion Fatigue Short Scale. This group also scored highest on items 2 and 7. Besides, item 9 (\u0026ldquo;I feel frustrated with my job\u0026rdquo;) showed the largest score difference, indicating professional confusion, fatigue, and emotional distress. This may stem from heightened sensitivity to patient suffering and death, leading to increased burnout and frustration (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). Additionally, the inability to establish stable relationships with supervisors and staff during a short internship exacerbates their emotional burden (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). Therefore, mindfulness training to enhance self-awareness and emotional management, along with a well-established professional and peer support system to provide continuous guidance and emotional support, is crucial to prevent the development of higher levels of compassion fatigue (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe \u0026ldquo;severe compassion fatigue\u0026rdquo; group, representing 12.10% of the participants, had average scores of 55.92 for burnout and 32.83 for secondary traumatic stress, with a total score of 88.75. This group scored highest on items 2 and 7, and showed the largest score differences on item 8 (\u0026ldquo;I have intrusive thoughts about the traumatic situations I have encountered\u0026rdquo;) and item 10 (\u0026ldquo;I relive traumatic experiences when helping those in crisis\u0026rdquo;), indicating rapidly increasing psychological trauma and distress. These interns lack effective coping strategies and psychological resilience, leading to higher secondary traumatic stress and compassion fatigue (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e). Long-term high-pressure environments and cumulative emotional burdens further aggravate these issues (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e). Therefore, psychological counseling, support groups, stress management and coping skills training, and crisis peer support workshops are crucial for managing these high-pressure environments and traumatic situations(\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eInfluencing factors of compassion fatigue among nursing students during the internship\u003c/h2\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003eDemographic characteristics\u003c/h2\u003e \u003cp\u003eOur study identified several demographic factors associated with higher compassion fatigue: being female, specializing in midwifery, having frequent night shifts, and intending to switch to another profession after graduation.\u003c/p\u003e \u003cp\u003eFemale students reported higher compassion fatigue than males. Sacco et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e) found similar results among critical care nurses. Female students generally possess higher emotional intelligence and empathy(\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e), making them emotionally engaged with patients, leading to emotional exhaustion and compassion fatigue over time (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e). Additionally, cultural norms in China discourage emotional expression in males, making male students less likely to engage deeply emotionally, reducing their risk of compassion fatigue (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMidwifery students reported higher compassion fatigue than nursing students. Despite working in generally positive obstetric environments, midwifery students frequently encounter complex or critical childbirth situations and neonatal deaths, leading to significant emotional strain (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e). They also form strong empathetic bonds with patients, increasing their risk of secondary traumatic stress (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e). Early emphasis on normal physiological birth may mislead students about the challenges of adverse events, resulting in increased emotional pressure and fatigue (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudents with frequent night shifts reported higher compassion fatigue scores, consistent with findings among hospice nurses (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e). Night shifts increase clinical knowledge and independence but also cause constant stress, emotional exhaustion, and exploitation or bullying, leading to anxiety and depression (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudents intending to leave the profession after graduation reported higher compassion fatigue. This lack of intrinsic motivation and misalignment between expectations and reality during internships leads to emotional exhaustion and moral distress (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e). Educators should encourage reflection on the nursing role to establish realistic career expectations and provide necessary support (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section4\"\u003e \u003ch2\u003ePsychological resilience\u003c/h2\u003e \u003cp\u003ePsychological resilience was found to be a protective factor against compassion fatigue. Higher psychological resilience was associated with a lower risk of developing compassion fatigue, aligning with previous research (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e). Psychological resilience enhances nursing students\u0026rsquo; psychosocial functioning and professional performance (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e), avoids emotion-centered coping, and builds confidence in managing workplace stress, thereby mitigating compassion fatigue and facilitating adaptation to the clinical environment (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e). As advocated by the American Association of Colleges of Nursing, nursing education should emphasize resilience development to improve overall well-being of nursing students (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003eProfessional identity\u003c/h2\u003e \u003cp\u003eHigher professional identity was associated with lower compassion fatigue. This negative correlation between compassion fatigue and professional identity has also been validated in similar groups (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e). Professional identity acts as a psychological resource, helping nursing students maintain a positive attitude during high-pressure and high-risk internship tasks and reducing negative emotional impact (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e). Decreased professional identity leads to reduced work enthusiasm and satisfaction, increasing susceptibility to compassion fatigue (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eSocial support\u003c/h3\u003e\n\u003cp\u003eGreater social support was linked to lower risk of compassion fatigue, consistent with findings reported by a study involving 307 intern nursing and midwifery students (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Social support enhances resilience, alleviates stress, loneliness, and anxiety (\u003cspan additionalcitationids=\"CR97\" citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e). According to the stress coping model, social support is a crucial external resource for coping with stress (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e). When students receive support from family, friends, and mentors, they may gain confidence and courage to employ positive coping strategies, thus mitigating negative emotions and preventing compassion fatigue (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e). Therefore, it is essential to develop and refine social skills development strategies related to support networks for nursing students, guiding them to actively seek social support when facing internship pressures.\u003c/p\u003e\n\u003ch3\u003eCoping style\u003c/h3\u003e\n\u003cp\u003eNegative coping strategies significantly and positively predict higher compassion fatigue, while positive coping strategies negatively predict lower compassion fatigue. Similar findings have also been validated in Rui's study on clinical nurses (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e). Coping strategies influence occupational stress and mental health (\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e). The demanding internship tasks and academic pressures consume a substantial amount of energy, and as resources are continuously depleted, negative emotions gradually emerge (\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e). Nursing students who adopt negative coping strategies are more likely to develop negative thoughts and avoidance behaviors, thereby increasing the risk of compassion fatigue (\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e). In contrast, positive coping strategies help nursing students maintain a positive attitude and mental health by effectively managing stressful events and mobilizing resources, thereby mitigating the impact of compassion fatigue (\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003eSelf-efficacy\u003c/h2\u003e \u003cp\u003eHigher self-efficacy was associated with lower compassion fatigue, consistent with findings in other nurse populations (\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e). Self-efficacy fosters a positive perception of nursing, stronger professional identity, and better career preparation (\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e), enhancing job satisfaction and reducing burnout (\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e). Therefore, nursing educators should guide students in seeking meaningful experiences and learning opportunities to build self-efficacy, improve adaptability, and reduce the incidence of compassion fatigue. Abusubhiah et al. (2023) advocate for the use of multimodal interventions, including flipped classrooms, simulations, debriefing, and role-playing, to better enhance nursing students\u0026rsquo; self-efficacy (\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDevelopment and application of a machine learning-based predictive model for compassion fatigue among nursing students during internships\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo our knowledge, this study is the first to develop a user-friendly, personalized model to predict compassion fatigue among nursing interns. Utilizing machine learning techniques, we confirmed the accuracy of this model, which contributes to optimizing clinical management. Additionally, our study identified nine independent risk factors for compassion fatigue: gender, specialty, frequency of night shifts, career intention, resilience scores, professional identity scores, social support scores, coping style scores, and self-efficacy scores. Recently, a study based on 21 tertiary hospitals in China developed a risk prediction model for compassion fatigue among emergency department nurses using a logistic regression model (\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e). This model included only seven factors: job position, job satisfaction, dietary habits, daily sleep duration, occupational stress, physical harassment, and workplace violence levels. In contrast, our model demonstrated higher predictive efficiency, covering a broader range of influencing factors, and providing a more comprehensive assessment of the risk of compassion fatigue. However, our model has not yet been validated in external populations, so its external validity may need further confirmation.\u003c/p\u003e \u003cp\u003eOur prediction model can identify nursing students at high risk of compassion fatigue before symptoms manifest. In clinical applications, the online measurement tool can be combined with existing compassion fatigue scales to provide a comprehensive assessment of nursing students. For instance, a student currently showing no symptoms of compassion fatigue may score zero on the scale, but this does not mean they will not experience compassion fatigue in the future. Our online tool calculates the probability of each student developing compassion fatigue, serving as a powerful supplement to existing scales and assisting nursing educators and clinical managers in devising appropriate interventions to support students' health and professional development.\u003c/p\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003eStrengths and limitation\u003c/h2\u003e \u003cp\u003eThis study has yielded some promising findings, primarily due to the following advantages. First, there are few existing machine learning prediction models specifically addressing compassion fatigue in nursing students during the internship, and our study fills this gap. Second, this study innovatively combines LPA and machine learning to identify different risk groups among nursing students through LPA and to predict individual risk levels using machine learning models. The application of machine learning algorithms enhances the predictive power of the model, offering advantages over traditional risk prediction methods, and aids school and hospital administrators in identifying high-risk individuals for compassion fatigue and implementing targeted interventions.\u003c/p\u003e \u003cp\u003eHowever, this study also acknowledges several limitations. First, the cross-sectional design may limit causal inference and introduce selection bias. Second, the relatively small sample size and lack of external validation may affect the robustness of the model. Therefore, further prospective studies with larger and more diverse samples are needed to evaluate the diagnostic sensitivity and specificity of the model. Third, the selection of input data and model parameters may vary when encountering new data, necessitating further research. Finally, the routine collection of psychosocial parameters in clinical settings may be challenging, potentially hindering the practical application of the model. Therefore, developing predictive models that include only objective parameters with high predictive performance would be a beneficial approach.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study developed a predictive model integrating demographic characteristics, work-related, and psychosocial variables, demonstrating excellent calibration and predictive capability for compassion fatigue among nursing students. The resulting online tool can screen students at moderate to high risk during internships, aiding nursing managers in optimizing prevention strategies and reducing incidence.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLPA, latent profile analysis\u003c/p\u003e\u003cp\u003eLMR, Lo-Mendell-Rubin likelihood ratio test\u003c/p\u003e\u003cp\u003eBLRT, bootstrap likelihood ratio test\u003c/p\u003e\u003cp\u003eAIC, Akaike Information Criterion\u003c/p\u003e\u003cp\u003eBIC, Bayesian Information Criterion\u003c/p\u003e\u003cp\u003eaBIC, adjusted Bayesian Information Criterion\u003c/p\u003e\u003cp\u003eLASSO, the Least Absolute Shrinkage and Selection Operator\u003c/p\u003e\u003cp\u003eAUC, area under the receiver operating characteristic curve\u003c/p\u003e\u003cp\u003eROC, receiver operating characteristic\u003c/p\u003e\u003cp\u003eDCA, Decision curve analysis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInstitutional Review Board approval was granted for this study by the institution review board of Hunan Traditional Chinese Medical College, with an approval number of YX202212001. Informed consent was obtained from all participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author[LJY] on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by 2024 Hunan Provincial Universities Ideological and Political Work Quality Project (Practice Education)[ grant number:24JP039].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLIY and XT conceived and designed the study. LJY, TS, and XT contributed to the acquisition, analysis, and interpretation of data. LJY, YL, JJZ, and MFJH were involved in investigation, methodology, and data curation. LJY drafted the manuscript. TX revised subsequent drafts. All authors reviewed and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThank you to all nursing interns who volunteered to participate in this study and also to the full-time student counselors for their great support in data collection. At the same time, we are grateful for the hard work of the editors and the valuable suggestions of the reviewers.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCheng J, Cui J, Yu W, Kang H, Tian Y, Jiang X. Factors influencing nurses' behavioral intention toward caring for COVID-19 patients on mechanical ventilation: A cross-sectional study. 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Int J Nurs Stud. 2023;148:104613.\u003c/span\u003e\u003c/li\u003e\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-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Nursing student, Internship, nonmedical, Compassion fatigue, Prediction model, Latent profile analysis, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-4709842/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4709842/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eExposure to compassion fatigue during internships can significantly impact on nursing students\u0026rsquo; future career trajectories and their intention to stay in the nursing profession. Accurately identifying nursing students at high risk of compassion fatigue is vital for timely interventions. However, existing assessment tools often fail to account for within-group variability and lack predictive capabilities. To develop and validate a predictive model for detecting the risk of compassion fatigue among nursing students during their placement.\u003c/p\u003e\u003ch2\u003eDesign:\u003c/h2\u003e \u003cp\u003eA cross-sectional study design.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData from 2256 nursing students in China between December 2021 and June 2022 were collected on compassion fatigue, professional identity, self-efficacy, social support, psychological resilience, coping styles, and demographic characteristics. The latent profile analysis was performed to classify compassion fatigue levels of nursing students. Univariate analysis, least absolute shrinkage and selection operator regression analysis were conducted to identify potential predictors of compassion fatigue. Eight machine learning algorithms were selected to predict compassion fatigue, and the performance of these machine learning models were evaluated using calibration and discrimination metrics. Additionally, the best-performing model from this evaluation was selected for further independent assessment.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA three-profile model best fit the data, identifying low (55.73%), moderate (32.17%), and severe (12.10%) profiles for compassion fatigue. The area under the curve values for the eight machine learning models ranged from 0.644 to 0.826 for the training set and from 0.651 to 0.757 for the test set. The eXtreme Gradient Boosting performed best, with area under the receiver operating characteristic curve values of 0.840, 0.768, and 0.731 in the training, validation, and test sets, respectively. SHAP analysis clarified the model\u0026rsquo;s explanatory variables, with psychological resilience, professional identity, and social support being the most significant contributors to the risk of compassion fatigue. A user-friendly, web-based prediction tool for calculating the risk of compassion fatigue was developed.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe eXtreme Gradient Boosting classifier demonstrates exceptional performance, and clinical implementation of the online tool can provide nursing managers with an effective means to manage compassion fatigue.\u003c/p\u003e","manuscriptTitle":"Development and validation of a machine learning-based predictive model for compassion fatigue in nursing interns: A cross-sectional study with latent profile analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-08 08:19:13","doi":"10.21203/rs.3.rs-4709842/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-09T12:07:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-03T14:00:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293047455059996742031597289247381521249","date":"2024-08-23T16:04:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-08T13:30:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179052158917246193848099376568092518534","date":"2024-08-07T18:59:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-05T13:55:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-16T11:17:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-15T10:46:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-15T10:45:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2024-07-09T07:07:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d04c62d3-09f3-4976-935d-72c527ca454d","owner":[],"postedDate":"August 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:08:41+00:00","versionOfRecord":{"articleIdentity":"rs-4709842","link":"https://doi.org/10.1186/s12909-024-06505-9","journal":{"identity":"bmc-medical-education","isVorOnly":false,"title":"BMC Medical Education"},"publishedOn":"2024-12-19 15:58:17","publishedOnDateReadable":"December 19th, 2024"},"versionCreatedAt":"2024-08-08 08:19:13","video":"","vorDoi":"10.1186/s12909-024-06505-9","vorDoiUrl":"https://doi.org/10.1186/s12909-024-06505-9","workflowStages":[]},"version":"v1","identity":"rs-4709842","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4709842","identity":"rs-4709842","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}