Background
Burnout is a state of physical and mental exhaustion caused by an individual’s prolonged exposure to work stress [1]. Maslach suggested [2] that burnout consists of three main dimensions: emotional exhaustion, depersonalization, and a diminished sense of accomplishment. Among them, emotional exhaustion is the core feature of burnout. It manifests as individuals experiencing excessive exhaustion and strain and a lack of enthusiasm for their work, which is usually the most obvious symptom. Depersonalization is manifested by individuals actively distancing themselves from the people they work with and displaying an attitude of indifference and neglect. In addition, diminished personal accomplishment refers to a decrease in an individual’s perception of his or her abilities and accomplishments at work, which leads to a negative evaluation of himself or herself [3].
Burnout is particularly prominent in the nurse population, which stems from the high-intensity work environment and the demands of emotional labor that make nurses more susceptible to this state [4, 5]. As the nursing profession continues to evolve, nurse burnout has become an urgent public health problem worldwide [6]. Studies have shown significant differences in the prevalence of burnout among nurses in different countries. A meta-analysis [7] revealed that the global prevalence of nurse burnout is 30%. Specifically, a study by Rezaei [8] reported that the prevalence of burnout among Iranian nurses was 36%. In the United States, the overall prevalence of nurse burnout is 31.5% [9]. In contrast, the overall prevalence of nurse burnout in China is as high as 64.5%, of which the prevalence of severe burnout is 12.5% [10]. This burnout not only causes significant psychological and physical health problems for individual nurses, such as anxiety, depression, and insomnia but also negatively affects the quality of care and patient satisfaction. According to findings from relevant studies in China [11, 12], burned-out nurses are more likely to make medical errors, leading to poor patient outcomes and decreased satisfaction. In addition, a nurse population with high burnout rates also increases the operational costs of the healthcare system, such as increased nursing staff turnover, leading to increased recruitment and training costs, affecting overall healthcare efficiency and quality. Moreover, the World Health Organization (WHO) noted in 2020 [13] that the global shortage of nurses was approximately 5.9 million, and most nurses were concentrated in low-income countries. Considering the profound impact of burnout on the physical and mental health of nurses and the quality of care, it is particularly important to gain an in-depth understanding of the current situation of nursing burnout. This will help draw more attention from all sectors of society to the mental health and working conditions of nurses.
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Studies have shown that the causes of nurse burnout can be summarized as individual and organizational factors [14]. Individual factors include age, gender, marital status, education level, and personality traits [14‐16], whereas organizational factors cover a wide range of aspects, such as the hospital’s work environment, staffing, interpersonal relationships, organizational support, and management policies [8, 17]. Moosavian Hiaq noted [18] that, with increasing age, the physical work capacity of older nurses significantly decreases, but their burnout levels are relatively low. Li’s study revealed [1] that nurses who work longer shifts and are sleep deprived are more likely to experience burnout, and these nurses have higher levels of job dissatisfaction and increased intentions to leave their jobs. In addition, Mao’s study showed [19] that teamwork had a positive impact on burnout and that the support nurses received from their coworkers significantly reduced emotional exhaustion.
Latent profile analysis (LPA) is an important and innovative methodology that uses an individual-centered perspective to explain the relationships among episodic continuum indicators through latent categorical variables [20]. The main goal of LPA is to classify the study population on the basis of differences in individual responses to the episodic items and to delve deeper into the differences in and characteristics of the correlational indicators among the groups. The method divides a set of observed variables into mutually exclusive subgroups or profiles that are locally independent, i.e., the variables within the profiles are independent of each other. LPA focuses on categorizing the different characteristics of a sample and emphasizes the individual level of analysis, which enables the identification of different subgroups of burnout. This provides a more nuanced perspective on nurse burnout research and contributes to a deeper understanding of the complexity of the phenomenon.
Tian et al. [21]. applied the cluster analysis method to study the burnout situation of neurologists and identified two subgroups: the low-achievement type and the emotionally depleted/depersonalized type. However, that study focused only on burnout among neurologists, and considering the specificity of burnout among nurses, it is necessary to use the LPA approach to explore the characteristics of burnout among nurses. Therefore, this study aimed to use LPA to analyze the characteristics of nurse burnout and its predictors in detail. Through the identification of different subgroups, the multidimensional factors affecting burnout will be revealed, providing a scientific basis for the development of more targeted interventions. These measures help improve the working environment of nurses and enhance their mental health, which in turn improves the quality of care and job satisfaction.
Methods
Study design and participants
This study utilized a cross-sectional design. The study period was from May to June 2024, and the study sites were three tertiary hospitals in Huai’an, Huzhou, and Taizhou, which targeted the clinical nurse population. Participants were recruited via convenience sampling, a method chosen for its suitability in capturing a diverse group of nurses working under varying conditions within the targeted hospitals. The convenience sampling approach was deemed appropriate given the pragmatic constraints of conducting a large-scale survey across multiple geographically dispersed sites. This method allowed for efficient data collection within the limited timeframe of the study. Kendall’s principle of sample size estimation [22] was followed; i.e., the sample size should be at least 5–10 times the number of study variables. The demographic questionnaire used in this study contains 10 variables, whereas the burnout scale contains 15 variables, totaling 25 variables, whereby the required sample size was initially determined to range from 125 to 250 cases. To increase the reliability of the study, the possibility of missing data or invalid questionnaires was predicted in advance, an invalidity rate of 20% was set, and the target sample size was adjusted accordingly, calculated as n = (125–250) ÷ (1–20%) = 157–313 cases. After a rigorous and meticulous screening process, 500 clinical nurses who fully met the preset inclusion and exclusion criteria were ultimately successfully attracted to participate in this study. The inclusion criteria were as follows: (1) held a valid nursing license; (2) had at least six months of practical experience in clinical nursing; and (3) clearly defined the purpose of the study and voluntarily signed the informed consent form. The exclusion criteria, on the other hand, covered nurses who were in an internship, had a standardized training period or were out for further training, as well as nurses who were unable to be on duty during the study period for personal reasons (e.g., personal leave, maternity leave, sick leave, etc.).
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Measures
General information questionnaire
The demographic questionnaire designed by the research team itself included several variables, such as age, years of working experience, gender, professional title, education level, marital status, religious affiliation, clinical department, position and number of night shifts per month.
Maslach burnout inventory-general survey (MBI-GS)
The scale utilized in this study is an adapted version of the Maslach Burnout Inventory-General Survey (MBI-GS) [23]. The translation and cultural adaptation of the scale into Chinese were conducted by Li [24], ensuring that the content and nuances of the original scale were preserved while being culturally relevant to the Chinese context. The scale comprises three core dimensions: emotional exhaustion, depersonalization, and a reduced sense of accomplishment, encompassing a total of 15 items. Each item was scored on a seven-point Likert scale, ranging from 0 (not at all compliant) to 6 (fully compliant). Notably, the factor of reduced sense of accomplishment was scored in reverse, whereas emotional exhaustion and depersonalization followed the principle of positive scoring. The total score derived from this scale provides a direct indication of the severity of emotional exhaustion, depersonalization, and the extent of personal accomplishment deficiency experienced by the respondents.
In addition to the translation and cultural adaptation, the current study conducted a rigorous validation process to further establish the appropriateness and reliability of the scale within the Chinese nursing population. Before its application in this study, the scale underwent pilot testing with a sample of Chinese nurses to assess its feasibility and comprehensibility. Furthermore, the overall Cronbach’s alpha coefficient of the scale in the present study was calculated to be 0.859, providing strong evidence of its high internal consistency and reliability. This validation supports the use of the MBI-GS as a reliable and appropriate tool for assessing burnout among Chinese nurses.
Data collection
The data collection task of this study was carried out by four professional researchers who underwent comprehensive and systematic training. The core of the training focused on the overall planning and execution process of the survey, the precise interpretation of the questionnaire items, and how to maintain a consistent and noninducing interpretation in data collection practices to ensure data quality. In the screening of research subjects, the researchers strictly followed the established inclusion and exclusion criteria. For questionnaire completion, standardized instructions were used, and the process of distributing and collecting the questionnaires was implemented onsite. For any omissions found in the questionnaires, the researchers immediately communicated with the respondents to confirm and complete the questionnaires to ensure that the data collected were valid. In the end, a total of 500 questionnaires were distributed to the target group, and 470 valid questionnaires were successfully collected, for an effective recovery rate of 94%.
Ethical considerations
This research project was formally approved by the Taizhou Hospital of Zhejiang Province Ethics Committee under the ethical approval number KL20231006. Before the investigation commenced, the research team provided each potential participant with a thorough disclosure of information, which set out the specific content of the study, the expected objectives, and the positive outcomes and benefits that they could enjoy as a result of their participation. The study strictly adhered to ethical principles, and by ensuring that each participant voluntarily signed an informed consent form based on a full understanding of the study information, it effectively guaranteed that their rights and interests would be fully respected and protected.
Data analysis
The study was conducted via SPSS 25.0 for data compilation and statistical analysis, in which measurements were analyzed using the mean ± standard deviation, while counts were expressed as frequencies and percentages. A two-sided α = 0.05 was set, and P < 0.05 was used as the criterion for statistical significance.
In addition, latent profile modeling was performed via MPLUS 8.3. The best classification model was determined on the basis of a combination of the Bayesian information criterion (BIC), Aicheck information criterion (AIC), Lo‒Mendell‒Rubin (LMR), adjusted Bayesian information criterion (aBIC), entropy index, and bootstrap-based likelihood ratio test (BLRT). Specifically, the smaller the values of the AIC, BIC, and aBIC are, the better the model fit [25]; when the entropy value is greater than 0.80, the classification accuracy is greater than 90% [26], whereas the BLRT and LMR are used to compare the difference in goodness-of-fit between the two models, and if the test result is significant (p < 0.05), the K-category model is significantly better than the K-1-category model [27].
Finally, the chi-square test and one-way ANOVA were used to compare the differences in general information about burnout among nurses with different profiles, whereas multiple logistic regression analyses were used to explore the predictors of burnout among nurses with different profiles.
Results
Demographic characteristics of the participants
A total of 470 clinical nurses were included in this study; the participants’ ages ranged from 21 to 53 years, and their years of clinical experience ranged from 1 to 34 years. Among these participants, 92.1% were female, 95.5% had no religious affiliation, 67.2% had a bachelor’s degree, 56.0% were married, 62.6% had a junior title, and 31.3% were engaged in surgical nursing, while the percentage of general clinical nurses reached 93.2%. In addition, 57.4% of the nurses worked five or more night shifts per month. Additional details about the participants are shown in Table 1.
Table 1
Demographic characteristics of the participants
Variable | N (%) | M (SD) |
|---|---|---|
Age | 30.99 (6.42) | |
Years of work experience | 8.86 (7.29) | |
Gender | ||
Male | 37 (7.9) | |
Female | 433(92.1) | |
Religion | ||
Yes | 21 (4.5) | |
No | 449 (95.5) | |
Educational | ||
College or below | 115 (24.5) | |
Bachelor | 316 (67.2) | |
Master | 39 (8.3) | |
Marital status | ||
Married | 263 (56.0) | |
Single | 207 (44.0) | |
Professional title | ||
Junior | 294 (62.6) | |
Intermediate | 142 (30.2) | |
Advanced | 34 (7.2) | |
Clinical unit | ||
Medical | 147 (31.3) | |
Surgical | 122 (26.0) | |
Emergency | 22 (4.7) | |
ICU | 139 (29.6) | |
Other | 40 (8.5) | |
Position | ||
Regular nurse | 438 (93.2) | |
Director, Deputy director, Head nurse, or Assistant head nurse | 23 (4.9) | |
Administrative and other | 9 (1.9) | |
Number of night shifts per month | ||
<5 | 200 (42.6) | |
≥ 5 | 270 (57.4) | |
Nurse burnout scores
The total burnout score among the nurses was 41.07 ± 13.46, of which the emotional depletion score was 15.31 ± 6.55, the depersonalization score was 10.74 ± 5.83, and the decrease in the accomplishment score was 15.02 ± 6.05.
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Latent profile analysis and naming of burnout among nurses
In this study, 15 items of the nurse burnout scale were used as exogenous variables to construct a model with one to five latent profiles, and the related LPA results are shown in Table 2. The results of the latent profile analysis revealed that as the number of latent profiles in the model increased, the goodness-of-fit indices (e.g., AIC, BIC, and aBIC) gradually decreased. According to the steep slope plot shown in Fig. 1, the classification model has an inflection point between the three- and four-profile models, at which time the decreasing trend tends to stabilize. In addition, the three-profile model had the highest entropy value (entropy = 0.941). Although the model was found to be statistically significant between the two and three profiles by the LMR and BLRT tests, the p value of the LMR did not reach a significant level in the four-profile model. Considering the interpretability and practical application value of the model, the three-profile model was finally chosen as the best classification scheme in this study.
Table 2
Fitting indices for latent profile analysis of burnout in nurses
Model | Loglikelihood | AIC | BIC | aBIC | Entropy | LMR | BLRT | Profile probability |
|---|---|---|---|---|---|---|---|---|
1-profile | -13060.952 | 26181.904 | 26306.486 | 26211.272 | N/A | N/A | N/A | 1 |
2-profile | -12083.008 | 24258.016 | 24449.042 | 24303.047 | 0.893 | 0.045 | < 0.001 | 0.52/0.48 |
3-profile | -11423.956 | 22971.911 | 23229.381 | 23032.605 | 0.941 | 0.031 | < 0.001 | 0.26/0.52/0.22 |
4-profile | -11076.832 | 22309.664 | 22633.577 | 22386.020 | 0.937 | 0.087 | < 0.001 | 0.22/0.43/0.20/0.15 |
5-profile | -10873.164 | 21934.328 | 22324.685 | 22026.347 | 0.933 | 0.408 | < 0.001 | 0.14/0.17/0.35/0.20/0.14 |
Fig. 1
Latent profile model fitting index steep slope plot of the MBI-GS. Note: AIC: Akaike information criteria; BIC: Bayesian information criteria; aBIC: adjusted Bayesian information criterion
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Figures 2 and 3 show the mean scores and response probabilities for the three latent profiles of nurse burnout across the 15 items, respectively. Profile 1 accounted for 26.2% of the total sample. This profile scored low on the depersonalization factor, which implies that nurses who maintain good emotional engagement in their work usually have a more positive relationship with their patients and do not show apathy or detachment. However, despite high emotional engagement, the scores on the reduced fulfillment factor showed that nurses felt less professional fulfillment. This reflects nurses’ deficiencies in terms of fulfillment and professional identity. Therefore, the profile was named the low depersonalization with low achievement burnout group. Profile 2, with a percentage of 52.1%, had moderate scores on all three factors of emotional exhaustion, depersonalization, and reduced accomplishment, indicating that nurses experienced some degree of stress and exhaustion during their careers but had not yet reached the level of high burnout. Therefore, this profile was referred to as the overall moderate burnout group. Profile 3 accounted for 21.7%, and this profile scored high on the emotional exhaustion factor, indicating that the nurses experienced significant emotional exhaustion, which manifested itself as emotional exhaustion and a lack of motivation toward their work. Moreover, this group scored low on achievement, further emphasizing that nurses experience a lack of professional fulfillment, which may lead to reduced satisfaction and motivation toward their work. Therefore, the profile was named the high emotional exhaustion with low achievement burnout group.
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Fig. 2
Latent profile analysis chart of nurse burnout. Note: F1: Emotional exhaustion, Item 1 to Item 5; F2: Depersonalization, Item 6 to Item 9; F3: Decreased sense of accomplishment, Item 10 to Item 15
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Fig. 3
Response probabilities of the 15 items of nurse burnout on the 3-profile model
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Single-factor analysis
The results revealed that the differences among the three profiles were statistically significant (P < 0.05) in terms of age, years of working experience, education, marital status, and number of night shifts per month. In contrast, the differences between the variables of gender, title, religion, clinical department in which they worked, and position were not statistically significant (P > 0.05) concerning the three profiles of burnout. See Table 3 for details.
Table 3
Bivariate analysis of the latent profile analysis of burnout in nurses
Variable | Low depersonalization with low achievement burnout group (n = 123) | Overall moderate burnout group (n = 245) | High emotional exhaustion with low achievement burnout group (n = 102) | F/X 2 | P |
|---|---|---|---|---|---|
Age | 32.57 ± 6.70 | 30.78 ± 6.62 | 29.60 ± 5.11 | 6.393 | 0.002 |
Years of work experience | 10.87 ± 7.73 | 8.62 ± 7.39 | 7.02 ± 5.87 | 8.303 | < 0.001 |
Gender | 2.914 | 0.233 | |||
Male | 6 (16.2) | 24 (64.9) | 7 (18.9) | ||
Female | 117 (27.0) | 221 (51.0) | 95 (21.9) | ||
Religion | 3.457 | 0.178 | |||
Yes | 3 (14.3) | 15 (71.4) | 3 (14.3) | ||
No | 120 (26.7) | 230 (51.2) | 99 (22.0) | ||
Education | 23.422 | < 0.001 | |||
College or below | 16 (13.9) | 75 (65.2) | 24 (20.9) | ||
Bachelor | 99 (31.3) | 155 (49.1) | 62 (19.6) | ||
Master | 8 (20.5) | 15 (38.5) | 16 (41.0) | ||
Marital status | 19.151 | < 0.001 | |||
Married | 89 (33.8) | 127 (48.3) | 47 (17.9) | ||
Single | 34 (16.4) | 118 (57.0) | 55 (26.6) | ||
Professional title | 5.953 | 0.203 | |||
Junior | 69 (23.5) | 153 (52.0) | 72 (24.5) | ||
Intermediate | 43 (30.3) | 73 (51.4) | 26 (18.3) | ||
Advanced | 11 (32.4) | 19 (55.9) | 4 (11.8) | ||
Clinical unit | 10.798 | 0.213 | |||
Medical | 34 (23.1) | 82 (55.8) | 31 (21.1) | ||
Surgical | 29 (23.8) | 57 (46.7) | 36 (29.5) | ||
Emergency | 7 (31.8) | 12 (54.5) | 3 (13.6) | ||
ICU | 45 (32.4) | 69 (49.6) | 25 (18.0) | ||
Other | 8 (20.0) | 25 (62.5) | 7 (17.5) | ||
Position | 3.977 | 0.409 | |||
General nurse | 112 (25.6) | 227 (51.8) | 99 (22.6) | ||
Director, Deputy director, Head nurse, or Assistant head nurse | 8 (34.8) | 13 (56.5) | 2 (8.7) | ||
Administrative and other | 3 (33.3) | 5 (55.6) | 1 (11.1) | ||
Number of night shifts per month | 21.657 | < 0.001 | |||
<5 | 74 (37.0) | 92 (46.0) | 34 (17.0) | ||
≥ 5 | 49 (18.1) | 153 (56.7) | 68 (25.2) |
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Multiple logistic regression analysis of the latent profile analysis of nurse burnout
Multiple logistic regression analysis was performed using the latent profile of nurse burnout as the dependent variable, and variables with P < 0.05 in the univariate analysis were selected as independent variables. In the comparison between the overall moderate burnout group and the low depersonalization with low achievement burnout group, nurses with more years of work experience (OR = 0.837, P < 0.05), a bachelor’s degree (OR = 0.360, P < 0.05), a master’s degree (OR = 0.246, P < 0.05), and a married marital status (OR = 0.387, P < 0.05) preferred to belong to the low depersonalization with low achievement burnout group, whereas nurses who were older (OR = 1.260, P < 0.05) and had 5 or more night shifts per month (OR = 2.261, P < 0.05) were more inclined to belong to the overall moderate burnout group. In a comparison between the high emotional exhaustion with low achievement burnout group and the low depersonalization with low achievement burnout group, nurses with more years of experience (OR = 1.283, P < 0.05), a bachelor’s degree (OR = 0.457, P < 0.05), and a marital status of married (OR = 0.433, P < 0.05) were more likely to belong to the low depersonalization with low achievement burnout group, whereas nurses who were older (OR = 1.283, P < 0.05) and had 5 and more night shifts per month (OR = 2.337, P < 0.05) were more likely to belong to the high emotional exhaustion with low achievement burnout group. The specific results are shown in Table 4.
Table 4
Multiple logistic regression analysis of the latent profile analysis of nurse burnout
Variable | Low depersonalization with low achievement burnout group (Ref) | |||||
|---|---|---|---|---|---|---|
Overall moderate burnout group | High emotional exhaustion with low achievement burnout group | |||||
OR | 95% CI | P value | OR | 95% CI | P value | |
Age | 1.260 | 1.047–1.517 | 0.014 | 1.283 | 1.029–1.600 | 0.027 |
Years of working experience | 0.837 | 0.714–0.981 | 0.028 | 0.799 | 0.660–0.967 | 0.021 |
Education (Ref: College or below) | ||||||
Master | 0.246 | 0.081–0.750 | 0.014 | 0.715 | 0.218–2.341 | 0.579 |
Bachelor | 0.360 | 0.195–0.665 | 0.001 | 0.457 | 0.220–0.947 | 0.035 |
Marital status (Ref: Single) | ||||||
Married | 0.387 | 0.208–0.722 | 0.003 | 0.433 | 0.207–0.904 | 0.026 |
Number of night shifts per month (Ref: <5) | ||||||
≥ 5 | 2.261 | 1.338–3.820 | 0.002 | 2.337 | 1.244–4.393 | 0.008 |
Discussion
Nursing burnout is a common challenge facing healthcare systems worldwide. Globally, the high prevalence of burnout among the nursing population not only affects the continuity and quality of healthcare services but may also lead to increased turnover and decreased job satisfaction among nurses. In this study, we applied LPA to explore in depth the classification of subgroups of nurse burnout in China and examined the predictors of each subgroup. The results revealed that nurses’ burnout could be classified into three subgroups: low depersonalization with low achievement burnout, overall moderate burnout, and high emotional exhaustion with low achievement burnout. There was significant heterogeneity among the subgroups. However, the classification results differed somewhat from those of Leiter’s [28] five-classification model, which may have been influenced by several factors. First, the selection of the study population and the sample size may affect the classification results. This study included nurses from different regions and professional backgrounds, resulting in diverse results. Second, differences in sociocultural backgrounds may also have an impact on nurses’ affective states and burnout manifestations, with differences in healthcare systems, work stress, and professional identity being potential influences [4, 29]. In addition, with changes in nurses’ work environment and the rapid development of the healthcare industry, the manifestation of burnout may change over time, further leading to differences in categorization models. Consistent with previous studies [30, 31], this study revealed the key factors influencing the subgroups of nurse burnout, including age, years of work experience, education level, marital status, and number of night shifts per month. These findings provide an important basis for a deeper understanding of the core causes of nurse burnout and provide important guidance for developing individualized interventions for nurses with different burnout subgroups.
Among the three burnout subgroups identified, the overall moderate burnout group accounted for the largest percentage, at 52.1%. Nurses in this group experienced some degree of burnout with moderate levels of all three factors: emotional exhaustion, depersonalization, and accomplishment. Although they had not yet reached the severe stage, emotional exhaustion gradually became apparent, and they began to lose their passion for their patients and their sense of accomplishment at work. This state may stem from prolonged periods of intense work and a lack of effective support systems. In the long run, these nurses may gradually lose empathy for their patients, leading to a decline in the quality of care. To address this problem, the first step is to conduct a comprehensive assessment of the workload, rationalize the working hours and shift system, reduce the intensity of work, and shorten the working hours. Implement a flexible and personalized shift system to minimize the frequency of night shifts for older nurses, especially those with high occupational stress, and encourage younger nurses to work shifts to ensure sufficient recovery time. Secondly, provided counseling resources and professional support to manage emotions and stress. Establish a mentorship program where experienced nurses mentor younger nurses to help them navigate stressful situations and build resilience. Finally, introduce wellness programs that include stress reduction techniques, sleep improvement strategies, and mental health counseling. Regular physical exams and health assessments should be conducted to monitor the physical and mental health of nurses [18].
In comparison, the high emotional exhaustion with low achievement burnout group accounted for the smallest percentage of the sample at 21.7%. Nurses in this subgroup faced significant emotional depletion and low achievement, exhibiting high levels of burnout and negative mood. Their emotional exhaustion limits their ability to maintain positive emotions at work, which may lead to a lack of patient care and feelings of detachment at work. In addition, the decline in professional fulfillment makes them feel helpless and lost, which not only affects nurses’ mental health but also may negatively impact the quality of patient care and teamwork. To respond effectively to this serious state of burnout, nursing administrators should adopt more in-depth and individualized interventions. First, regular mental health assessments should be conducted, and professional psychological counseling and therapy should be provided to help nurses address deep-seated emotional distress. Second, improve the working environment by increasing human resources and optimizing workflow to effectively reduce nurses’ work pressure. In addition, the social support network of married nurses was strengthened through family-friendly activities and emotional support services [19].
Finally, the low depersonalization and low achievement burnout group accounted for 26.2% of the total sample. In this subgroup, although nurses performed well in terms of depersonalization and were able to maintain good emotional connections with their patients, they scored low in achievement, indicating a lack of recognition of the value of their work, which may lead to a decrease in occupational satisfaction. A chronic lack of professional identity may cause nurses to lose enthusiasm for their jobs and even consider leaving. Therefore, nursing administrators should focus on improving their sense of professional identity. They can help reset their career goals by providing personalized career planning and mentor support while organizing team-building activities and social gatherings can help enhance the bonding and team atmosphere among nurses [32].
The study also revealed that nurses who were older and who worked five or more night shifts per month were more likely to belong to the overall moderate burnout group and the high emotional exhaustion with the low achievement burnout group. This is likely due to the combined effects of age and workload intensity; older nurses, who may already experience higher levels of fatigue due to accumulated career stress, are further exacerbated by frequent night shifts, leading to higher emotional exhaustion [15]. A study conducted in Iran reveals that the risk of experiencing burnout during their careers is particularly significant among nurses in the 21 to 30 age group. This reflects the unique work pressures and challenges faced by individuals at the beginning of their careers [18]. In contrast, nurses with more years of experience, who were married and had a bachelor’s degree, were more likely to belong to the low depersonalization with low achievement burnout group. This finding is similar to the results of a Polish study on the phenomenon of burnout among healthcare workers. While work experience increases expertise, it also exposes healthcare workers to additional stressors that further exacerbate the risk of burnout [15]. Meanwhile, this may also be attributed to the interaction between marital status and educational level; married nurses often benefit from strong social support networks, which can mitigate depersonalization [33]. Additionally, those with a bachelor’s degree typically possess better problem-solving skills and resources, allowing them to manage work pressures more effectively, thus reducing depersonalization tendencies [34]. Concerning the number of night shifts per month, nurses who frequently work night shifts are prone to suffer from disruptions in their biorhythms, which not only affects the quality of their sleep but also may lead to increased feelings of mental and physical exhaustion [35]. Notably, the interaction between the frequency of night shifts and educational level influences burnout severity. Nurses with higher educational levels may have better-coping mechanisms and resources to mitigate the impact of night shifts, whereas those with lower educational levels may find it more challenging to manage the same workload, leading to higher levels of burnout. However, Jarosz’s study showed that the level of education did not have a significant effect on burnout. Its findings only revealed a correlation between the number of forms of graduate education and disappointment [14]. Furthermore, nurses who work five or more night shifts per month experience high-intensity workloads and low levels of self-recovery, which can lead to more emotional exhaustion and lower levels of fulfillment, which in turn can lead to their belonging to the high emotional exhaustion and low fulfillment burnout group [36]. This is particularly pronounced in younger nurses, who may lack the experience and resilience to handle such demanding schedules, resulting in higher burnout levels.
In this study, nurses with undergraduate degrees were more likely to belong to the low depersonalization with low achievement burnout group than were those with college degrees or less, which may stem from their strengths in career aspirations, learning ability, and access to resources. This effect is particularly pronounced when combined with factors such as marital status and age. For instance, younger, single nurses with bachelor’s degrees may experience lower burnout due to their higher adaptability and better access to social support networks [35]. Conversely, older nurses with bachelor’s degrees may still experience higher burnout if they are also working frequent night shifts, highlighting the need for tailored interventions that consider these complex interactions. Undergraduate education typically covers a broader range of professional knowledge and practice skills, allowing these nurses to be more confident in their work and able to meet patient needs more effectively, thus reducing frustration and depersonalization tendencies due to a lack of competence. However, the impact of educational level on burnout is not uniform; the interaction between educational level and workload (such as night shifts) significantly influences burnout outcomes, indicating the need for more nuanced interventions that consider these factors together.
The results of this study provide important practice guidelines for nursing administrators and clinical nurses. Firstly, the introduction of a personalized shift system, particularly to reduce the frequency of night shifts for senior nurses and to encourage shift rotation to ensure that all nurses, especially younger and less experienced nurses, are given adequate recovery time. Secondly, social support networks were strengthened to assist married nurses through family-friendly activities and emotional support services, while a mentor-apprentice system was established whereby experienced nurses would mentor younger nurses to help them cope with stressful situations and increase resilience [16]. In addition, continuous education and training are provided to nurses with lower education levels to enhance their problem-solving and resource management skills, and specialized workshops on stress management and emotional resilience are held for nurses who frequently work night shifts [32]. At the same time, health and well-being programs including stress relief techniques, sleep improvement strategies, and mental health counseling are introduced, and the physical and mental health of nurses is monitored through regular medical checkups and health assessments. Finally, data analytics are utilized to identify patterns and trends in nurse burnout for more accurate and timely interventions, and predictive models are developed to anticipate high-risk burnout scenarios so that preventive measures can be proactively implemented.
Limitations
There are also several limitations to this study. First, the data were collected primarily on the basis of nurses’ self-assessments, which may have led to self-report bias. To assess nurse burnout more comprehensively and objectively, future studies should analyze both subjective and objective perspectives. Second, A major limitation of this study is the use of a convenience sampling method, which was conducted in only three hospitals in China, which may limit the generalizability of the findings to a wider population. There may be limitations to the applicability of the findings to other regions, especially given the different regional and cultural differences in China. In addition, hospital size and department-specific policies may also influence the extent and manifestation of burnout, and these potential confounding variables were not fully explored in the study. Future studies should consider using more rigorous sampling methods, such as random sampling, to enhance the external validity of the findings. Meanwhile, the study should further explore the role of factors such as hospital size and departmental policies in burnout and control for these potential confounding variables through a multicenter research design to improve the reliability of the study and the broad applicability of the findings. Finally, because this study used a cross-sectional design, we could not determine the stability of the burnout subgroup. Future studies could incorporate longitudinal analyses to more intuitively compare the transition probability across subgroups at different points in time.
Conclusion
This study analyzed the subgroups of burnout among Chinese nurses through the LPA method, identifying three main types of burnout and delving into the predictors. This classification provides a basis for nursing managers to develop individualized interventions and highlights the important role of factors such as age, years of working experience, marital status, education level, and number of night shifts in burnout. The findings also reveal the diversity of burnout and call for nurses to increase self-awareness and actively seek support. In addition, this study provides a new direction for further nursing research, suggesting that sustained attention should be paid to the multifaceted factors affecting burnout and its dynamics to better understand and address the challenges facing the nursing profession. Future research should further explore ways to mitigate the effects of burnout through organizational-level support measures, such as flexible work arrangements and mental health support programs, to enhance the quality of care and nurses’ job satisfaction.
Acknowledgements
The authors sincerely appreciate all 470 Chinese registered nurses for their participation in our questionnaire survey.
Declarations
Ethics approval and consent to participate
This study was approved by the ethics committee of Taizhou Hospital of Zhejiang Province (approval number: KL20231006), and all procedures were conducted in accordance with the Declaration of Helsinki. All the participants provided informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
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