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. 2024 Mar 26;11(3):e2146. doi: 10.1002/nop2.2146

Latent profiles of nurses’ subjective well‐being and its association with social support and professional self‐concept

Chuyuan Miao 1, Chunqin Liu 1, Ying Zhou 1,✉, Joanne W Y Chung 1,2,✉, Xiaofang Zou 3, Wenying Tan 1, Yu Ma 1, Qing Luo 1, Jiani Chen 1, Thomas Kwok Shing Wong 1,4
PMCID: PMC10966136  PMID: 38532303

Abstract

Aim

To identify latent profiles of nurses’ subjective well‐being (SWB) and explore its association with social support and professional self‐concept.

Design

This study used an online survey and cross‐sectional latent profile analysis design.

Methods

A total of 1009 nurses from 30 hospitals in Guangdong Province, China, were selected using convenience sampling. An online questionnaire survey comprising the following scales was distributed: Index of Well‐Being, Nurses’ Professional Self‐concept Questionnaire and Multidimensional Scale of Perceived Social Support. Nurses’ SWB was examined and categorized into profiles using nine Index of Well‐being items as explicit variables and ordinal logistic regression analysis was performed to explore factors related to the distinct categories.

Results

Nurses’ SWB was divided into four latent profiles: extremely low, low, moderate and high. Regression analysis showed that social support and professional self‐concept influenced SWB. There were statistically significant differences in age, title, working years, social support and professional self‐concept among nurses in the different well‐being categories. Ordered logistic regression analysis showed that social support and professional self‐concept are associated with different SWB profiles.

Keywords: latent profile analysis, nurses, professional self‐concept, social support, subjective well‐being

1. INTRODUCTION

Due to the impact of the COVID‐19 pandemic, nurses’ subjective well‐being (SWB) has been severely affected by an excessive workload and high work pressure, bringing them more negative effects related to their profession. SWB is defined as an individual’s subjective feelings about the happiness and satisfaction of their own lives (Campbell, 1976; Diener, 1984) and is a long‐standing pursuit (Seligman, 2011). As nurses are key to maintaining the stability of the healthcare system, their SWB is worthy of attention. Their well‐being not only has a significant impact on the health and well‐being of the public, but is also linked to their own quality of life and the development of the nursing workforce.

High SWB is conducive to mental health and positive mood, especially for nurses with strong professional identities and positive attitudes towards work (Ren et al., 2021). Moreover, due to the challenges of a worldwide shortage of nurses, along with the increasing demand for quality nursing care, focusing on the well‐being of nurses today remains an extremely important topic.

Presently, most studies on SWB have adopted a variable‐centred approach, such as calculating the total or average score on SWB scales and using regression analysis to describe the relationship between variables (Huang et al., 2021; Shi et al., 2020) which often tends to ignore individual heterogeneity. Furthermore, limited research has explored the factors associated with different categories of SWB, contributing to the lack of research depth.

Latent profile analysis (LPA) is a variable‐centred and artificially segmented statistical method that can help explore the underlying categorical structure of data by performing a cluster analysis on multiple variables (Sinha et al., 2021). The advantage of this method is that it ensures that the differences between categories are maximized and the differences within categories are minimized. It also estimates the proportion of each category in the overall group, thus capturing group inequalities that cannot be observed in a variable‐centred study. Furthermore, few studies have reported the different categories of nurses’ SWB using LPA methods and this method could help identify different types of characteristics, which is important for the subsequent categorization of interventions. Hence, considering the significance of SWB in the nurse population, identifying the latent profiles of nurses’ SWB and further exploring the associated factors of different profiles can contribute to the development of targeted well‐being enhancement strategies for different nurses.

2. BACKGROUND

Due to the impact of the shock pandemic, the overall well‐being of nurses has yet to improve. A survey of 1418 nurses during the COVID‐19 pandemic found that 47.4% perceived hazards to their well‐being (Munn et al., 2021). Another survey of 695 emergency nurses found that while 84.9% perceived high levels of well‐being, 32.7% experienced high levels of stress and an inclination to leave the profession (de Wijn et al., 2022). A large cross‐sectional study of 138,279 nurses in 243 hospitals in China during the COVID‐19 pandemic also found that negative emotions such as anxiety, depression and stress were prominent among Chinese nurses (Li et al., 2023).

However, to enhance nurses’ SWB, it is not sufficient to focus on the current state of SWB among nurses but also to examine the factors that affect it. Previous studies have found that nurses’ SWB is influenced by numerous factors such as personality traits, values, life experiences, family factors and the social environment (Bégat & Severinsson, 2006; Xiao et al., 2022). Additionally, demographic factors such as gender, age, education, income and employment also influence nurses’ SWB (Das et al., 2020). However, there are few studies on the influencing factors of different latent profiles of SWB, which may be one of the reasons for the lack of research depth. Therefore, to improve nurses’ SWB, we should not only pay attention to the latent profile of nurses’ SWB, but also identify the factors that influence it under different profiles to provide more specific intervention programmes for nurses.

Positive psychology identifies SWB as the primary goal to pursue in life. According to PERMA theory, the five elements of Positive emotions, Engagement, Relationships, Meaning and Accomplishment can all help to a person to achieve happiness and well‐being in their life (Seligman, 2011). Among the numerous factors that might influence nurses’ SWB, we chose to focus on social support and professional self‐concept so as to study one intrinsic and one extrinsic element, aiming to provide more targeted suggestions and actions for promoting the well‐being of different nurses.

Social support can be considered an important part of positive relationships and devoted to pursuing well‐being. Social support is an element of R (Relationships) in the PERMA theory and is an external element. Social support refers to the emotional and material support that individuals receive from others when facing pressures or challenges, generating feelings of care and attention are generated in the interaction between the individuals (Costa‐Cordella et al., 2021; Orgambídez & Almeida, 2020; Seligman, 2011). Previous studies have found that social support is also considered to be one of the factors that have a key impact on individual SWB (Cohen & Wills, 1985; Ersin et al., 2022). However, most of the current studies have focused mainly on the effects of social support on nurses’ well‐being but less on the effects of social support on the well‐being of nurses under different profiles. Therefore, exploring the impact of social support on well‐being under different latent profiles will improve the understanding of the mechanism of social support on well‐being and provide a reference for providing effective social support strategies.

Moreover, given the specific needs of nursing, a key factor affecting nurses’ well‐being is their knowledge and perceptions of the nursing profession. Nurses’ professional self‐concept, an intrinsic element of M (Meaning) in the PERMA theory, is also one of the factors affecting the SWB of nurses in their professional role. Nurses’ professional self‐concept, which encompasses elements such as perceptions and experiences of themselves, their professional attitudes towards nursing and their values, is a central driver in their professional development (Cowin et al., 2008) and is related to work satisfaction (Li et al., 2021) and SWB (Céspedes et al., 2020).

To sum up, current studies mainly focused on the overall level of SWB and its associated factors. However, it is essential to consider the impact of heterogeneity (different profiles) among the nurse population. Therefore, this study aimed to use LPA to explore the latent profiles of nurses’ SWB and to explore the factors associated with different profiles, in the hope of helping nursing managers formulate targeted strategies to enhance nurses’ SWB from a new perspective.

3. METHODS

3.1. Design

This cross‐sectional LPA adhered to the STROBE checklist (Table S2).

3.2. Study setting and sampling

From January 2023 to April 2023, nurses from 30 public tertiary hospitals in the Guangdong Province region of China were surveyed online using convenience sampling. In China, tertiary hospitals have more advanced equipment and a higher number of medical staff. Nurses in tertiary hospitals have a higher workload due to the more complex and challenging disease treatment and medical tasks in these hospitals. Hence, focusing on nurses who work in such hospitals can help us to understand their expectations and needs regarding the work environment, support mechanisms and career development, which can then contribute to the development of more targeted interventions. The inclusion criteria were (1) having nursing qualifications and (2) voluntary participation in the study. The exclusion criteria were: (1) trainee or intern nurses, (2) nurses currently enrolled in further education or training programmes. Previous studies recommended that a reasonable minimum sample size should be approximately 500 for LPA methods (Spurk et al., 2020). Considering this was a multicentre study, a larger sample size was more suitable. Thus, 1009 nurses were included in this study.

3.3. Procedure

This study used a web‐based questionnaire. A QR code of the questionnaire was sent by the research team members to the leaders of the nursing departments or head nurses of clinical departments in several public tertiary hospitals in Guangdong Province. With their permission, the leader or the head nurse in each hospital forwarded the QR code to the nurses in each department and encouraged them to voluntarily complete the questionnaire. After obtaining informed consent, the first page of the questionnaire provided consistent instructions to the participants explaining the survey’s goals and procedures. Each survey question required an answer and the system prevented participants from skipping questions or providing multiple answers from the same device. We also excluded responses with regularity or logical confusion during the screening of questionnaires. For example, questionnaires in which participants chose the same option for all the answers would be excluded, as we had set the reverse scores for some questions. The data were downloaded from the platform of the Questionnaire Star platform by a research team member (CYM).

3.4. Ethical considerations

This study was approved by the Medical Ethics Committee of Guangzhou Medical University (No. 202210003). This study is part of a larger investigation registered in the Chinese Clinical Trial Registry (ChiCTR2200066699). The respondents were informed about the purpose and procedures of the study on the first page of our questionnaire and could withdraw from the study at any time. We provided an option for agreement or withdrawal; thus, the respondents had the right to choose to participate or not. This study was conducted according to the principles outlined in the Declaration of Helsinki (World Medical Association, 2013).

3.5. Measurements

3.5.1. Demographic questionnaire

A demographic questionnaire was designed by the researcher to gather relevant information on gender, age, marital status, education, position, title, department, working years, job category and average monthly earnings.

3.5.2. Index of well‐being (IWB) Scale

SWB was assessed using the Chinese version (Wang et al., 1999) of the Index of Well‐Being (IWB) Scale. The scale was introduced by Campbell et al. (1976) to measure nurses’ subjective satisfaction with their actual situation and well‐being. The scale consists of two components: an index of general affect (eight items) and an index of life satisfaction (one item). This scale is scored by multiplying the first eight item scores by 1 and the last item score by 1.1. The scale consists of nine items rated on a 7‐point Likert scale ranging from 2.1 to 14.7. Higher scores indicate higher perceived well‐being. Additionally, IWB is widely used by nurses, patients and students in China (Huang et al., 2021; Qun et al., 2019; Shi et al., 2020). In this study, the Cronbach’s alpha coefficient for the scale was 0.94.

3.5.3. Nurse Self‐Concept Questionnaire

The Nurse Self‐Concept Questionnaire (NSCQ), designed by Cowin (2001), was used to assess the professional self‐concept of nurses. The scale contains 6 dimensions (general self‐concept, caring, staff relations, communication, knowledge and leadership) and each dimension has 6 items, for a total of 36 items. All items are graded on a Likert scale from 1 to 8. Higher scores reflect higher professional self‐concepts. In this study, we used the validated and reliable translated Chinese version of the scale (Cao et al., 2013). The overall Cronbach’s alpha coefficient of the scale was 0.95. Cronbach’s alpha values for all the six dimensions used in this analysis were 0.96 (general self‐concept), 0.94 (caring), 0.94 (staff relations), 0.96 (communication), 0.94 (knowledge) and 0.95 (leadership).

3.5.4. Multidimensional Scale of Perceived Social Support

The Multidimensional Scale of Perceived Social Support (MSPSS) was developed by Zimet et al. (1988) to measure an individual’s feelings of being respected and supported. The Chinese version has been widely used for research (Lu et al., 2023). The scale comprises 12 items and has three dimensions: family, friends and significant others (e.g. leaders, co‐workers). It has a seven‐point Likert scale ranging from 1 (‘strongly disagree’) to 7 (‘strongly agree’). The total score ranges from 12 to 84. Higher scores indicate that individuals feel more social support. The Cronbach's alpha coefficient in this study was 0.97.

3.6. Statistical analyses

SPSS 25.0 and Mplus 8.3 statistical software were used for data analysis. For quantitative data that conformed to a normal distribution, statistical descriptions were in the form of mean and standard deviation (M ± SD) and t‐tests or one‐way analysis of variance (ANOVA) was used for comparisons between groups. For qualitative data, rates or percentages were used and two tests were used for comparisons between groups. Correlation analyses were performed using Pearson's correlation analysis (see Table S1).

Latent profiles of nurses’ SWB were analysed using Mplus 8.3. The main assessment indicators of the latent profile model are the Akaike information criterion (AIC), Bayesian information criterion (BIC), adjusted Bayesian information criterion (aBIC), bootstrap likelihood ratio test (BLRT) and entropy (Sinha et al., 2021). In this case, smaller AIC, BIC and aBIC values indicated a better fit of the model to the data. Additionally, LMR and BLRT can be used to compare the differences in fit between different categories. The p value should be less than 0.05 and the smaller the value, the better the model fit. The entropy value is used to evaluate the classification quality of the model; an entropy value greater than 0.8 indicates that the model classification will be clearer.

Due to nurses’ profiles being from 1 to 4, making them an ordinal categorical dependent variable and the parallelism test being greater than 0.05, an ordinal logistic regression was used to determine factors associated with different profiles of nurses’ SWB. Consequently, ordinal logistic regression analysis was used to examine the effects of demographic information, social support and professional self‐concept on SWB.

4. RESULTS

4.1. Demographic characteristics

The sample comprised 1009 participants, of which most were women (n = 970). The study presents the demographic distribution characteristics. The median age of the participants was (35.42 ± 8.14) years; Most of the participants had a high level of educational attainment (82.85% had a bachelor’s degree). More than half of the participants were married (70.07%), 28.44% of the participants were single and 1.49% were divorced or widowed. Most of them were clinical nurses (69.80%), 16.90% were nurse educators or nurse managers and 13.30% were head nurses or above. Over half (53.02%) of nurses held the title of ‘Senior Nurse’ and above. Participants worked in several clinical specialties, including departments such as internal medicine, surgery, paediatrics, obstetrics and gynaecology, and emergency room. Two hundred and twenty‐three nurses had less than 5 years’ work experience and 137 nurses had worked for 26 years and above. In terms of type of employment, 44.70% of them were contract employees, while just over half of the study participants (51.30%) held permanent employment contracts as nurses. The majority (78.89%) had a monthly income of ¥5000–15,000.

4.2. Latent profile of nurses’ SWB

In this study, nine items of nurses’ well‐being were used as exogenous variables to establish a latent profile model and 1–8 categories were used for model fitting. As shown in Table 1, as the number of categories increased, the AIC, BIC and aBIC values gradually decreased and the decrease gradually slowed down after four categories, indicating that the model fitting effect gradually improved. The entropy values were all greater than 0.9, which indicated that all eight models had good classification ability. The p‐values for the LMRT and BLRT were statistically significant. Considering the parsimony and balance of the models, the Class 4 was selected as the final model (Table 1).

TABLE 1.

Comparison of fit parameter indices of different latent profile models.

Class K AIC BIC aBIC LMR(p) BLRT(p) Entropy Sample proportion (%) per class
1 18 25797.76 25886.26 25829.09 – – – –
2 28 21045.86 21183.53 21094.60 <0.001 <0.001 0.95 41.23/58.77
3 38 19407.29 19594.13 19473.44 0.028 <0.001 0.92 45.19/15.56/39.25
4 48 18373.17 18609.17 18456.72 <0.001 <0.001 0.92 11.79/30.62/28.54/29.04
5 58 17904.59 18189.76 18005.54 0.0001 <0.001 0.93 3.96/10.70/26.36/30.03/28.94
6 68 17486.66 17820.99 17605.02 0.0001 <0.001 0.94 3.87/10.70/26.26/29.63/24.48/5.06
7 78 17260.08 17643.59 17395.85 0.003 <0.001 0.92 10.70/3.87/25.57/5.06/27.06/8.42/19.33
8 88 17058.12 17490.79 17211.29 0.342 <0.001 0.93 9.22/3.67/2.18/27.26/25.07/19.23/4.96/8.42
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Figure 1 is based on the standardized scores of the nine items of the IWB scale. The latent profiles of nurses’ SWB were classified into four categories. Category 4 accounted for 29.0% (293 nurses) but scored higher in all dimensions than the other three categories and thus had the highest level of well‐being, therefore it was named High SWB. Category 3 accounted for 30.6% (309 nurses) and scored at a moderate level on all dimensions; it was named Moderate SWB. Categories 2 and 1 scored below the mean line for each dimension. Category 2 accounted for 28.5% (288 nurses) and Category 1 accounted for 11.8% (119 nurses). Since Category 2 scored higher than Category 1 on all dimensions, Category 2 was named Low SWB, while Category 1 was named Extremely Low SWB (Figure 1).

FIGURE 1.

FIGURE 1

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Latent profile analysis of nurses’ SWB.

4.3. One‐way ANOVA of the factors influencing the different categories of nurses’ well‐being

Different categories of nurses’ SWB were used as subgroup variables, and one‐way ANOVA and chi‐square tests were used to compare the differences in SWB of nurses from different categories across different demographic profiles. The results showed that the differences in SWB of nurses from the four categories in terms of age, title, working years, social support and professional self‐concept were all statistically significant (p < 0.05) (Table 2).

TABLE 2.

Characteristic descriptions of nurses’ SWB with different profiles (N = 1009).

Variables Option Extremely Low SWB a Low SWB Moderate SWB High SWB χ 2/F p
Age (Mean ± SD) 35.14 ± 8.30 33.83 ± 7.42 35.42 ± 8.43 37.11 ± 8.15 F = 8.08 <0.001
Gender (N, %) Woman 110 (92.40%) 278 (96.50%) 300 (97.10%) 282 (96.20%) χ 2 = 5.26 0.15
Man 9 (7.60%) 10 (3.50%) 9 (2.90%) 11 (3.80%)
Marital status (N, %) Single 37 (31.10%) 97 (33.70%) 89 (28.80%) 64 (21.80%) χ 2 = 11.09 0.09
Married 80 (67.20%) 186 (64.60%) 216 (69.90%) 225 (76.80%)
Divorced or widowed 2 (1.70%) 5 (1.70%) 4 (1.30%) 4 (1.40%)
Education (N, %) Junior college or below 16 (13.40%) 51 (17.70%) 52 (16.80%) 54 (18.40%) χ 2 = 6.14 0.41
Bachelor's degree 100 (84.00%) 222 (77.10%) 250 (80.90%) 227 (77.50%)
Master's degree or above 3 (2.50%) 15 (5.20%) 7 (2.30%) 12 (4.10%)
Position (N, %) Clinical nurse 87 (73.10%) 214 (74.30%) 210 (68.00%) 193 (65.90%) χ 2 = 6.03 0.11
Nurse group leader or manager 32 (26.90%) 74 (25.70%) 99 (32.00%) 100 (34.10%)
Title (N, %) Nurse 19 (16.00%) 59 (20.50%) 49 (15.90%) 31 (10.60%) χ 2 = 24.78 <0.001
Senior nurse 43 (36.10%) 101 (35.10%) 90 (29.10%) 82 (28.00%)
Nurse in charge 43 (36.10%) 107 (37.20%) 146 (47.20%) 148 (50.50%)
Head nurse or above 14 (11.80%) 21 (7.30%) 24 (7.80%) 32 (10.90%)
Department (N, %) Surgery 22 (18.50%) 55 (19.10%) 51 (16.50%) 48 (16.40%) χ 2 = 13.66 0.88
Internal medicine 35 (29.40%) 75 (26.00%) 97 (31.40%) 83 (28.30%)
Paediatrics 7 (5.90%) 12 (4.20%) 11 (3.60%) 17 (5.80%)
Obstetrics and gynaecology 8 (6.70%) 26 (9.00%) 29 (9.40%) 37 (12.60%)
Intensive care unit 5 (4.20%) 14 (4.90%) 16 (5.20%) 7 (2.40%)
Emergency room 5 (4.20%) 8 (2.80%) 7 (2.30%) 8 (2.70%)
Operating room 4 (3.40%) 8 (2.80%) 7 (2.30%) 7 (2.40%)
Other departments 32 (27.10%) 88 (30.80%) 90 (29.20%) 84 (28.90%)
Job category (N, %) Permanent staff 56 (47.10%) 147 (51.00%) 153 (49.50%) 162 (55.30%) χ 2 = 6.05 0.42
Contract employees 55 (46.20%) 131 (45.50%) 142 (46.00%) 123 (42.00%)
Personnel agency employees 8 (6.70%) 10 (3.50%) 14 (4.50%) 8 (2.70%)
Average monthly earnings (Chinese, yuan) (N, %) <10,000 65 (54.60%) 143 (49.70%) 156 (50.50%) 135 (49.50%) χ 2 = 2.97 0.81
10,000–19,999 50 (42.00%) 134 (46.50%) 141 (45.60%) 144 (49.10%)
≥20,000 4 (3.40%) 11 (3.80%) 12 (3.90%) 14 (4.80%)
Working years (N, %) ≤5 years 30 (25.20%) 71 (24.70%) 74 (23.90%) 48 (16.40%) χ 2 = 33.46 0.004
6–10 years 21 (17.60%) 64 (22.20%) 45 (14.60%) 41 (14.00%)
11–15 years 25 (21.00%) 68 (23.60%) 66 (21.40%) 64 (21.80%)
16–20 years 13 (10.90%) 36 (12.50%) 47 (15.20%) 42 (14.30%)
21–25 years 16 (13.40%) 21 (7.30%) 39 (12.60%) 41 (14.00%)
≥26 years 14 (11.80%) 28 (9.70%) 38 (12.30%) 57 (19.50%)
Social support (Mean ± SD) 57.25 ± 14.69 60.27 ± 12.48 63.92 ± 10.85 69.82 ± 12.41 F = 42.74 <0.001
Professional self‐concept (Mean ± SD) 190.24 ± 52.10 208.81 ± 43.43 219.37 ± 42.17 237.25 ± 40.26 F = 40.08 <0.001
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Abbreviation: SD, standard deviation.

a

Extremely Low SWB, Low SWB, Moderate SWB and High SWB represent for the four latent profile of nurses’ SWB in this study.

4.4. Ordinal logistic regression analysis of factors influencing different categories of nurses' SWB

In this study, nurses’ SWB was used as the dependent variable and variables that were statistically significant in the univariate analysis were included in the analysis. The results of the parallel lines test showed χ 2 = 27.46, p = 0.19 > 0.05, suggesting that the proportional odds assumption exists and could be analysed using ordered logistic regression (Tables 3 and 4).

TABLE 3.

Assignment method of dependent variable (y) and independent variable (x).

Variable name Variable Meaning
Y SWB High SWB = 0, Moderate SWB = 1, Low SWB = 2, Extremely Low SWB = 3 a
X 1 Title Nurse = 0, Senior nurse = 1, Nurse in charge = 2, Head nurse or above = 3
X 2 Working years ≤5 years = 0, 6–10 years = 1, 11–15 years = 2, 16–20 years = 3, 21–25 years = 4, ≥26 years = 5
X 3 Age Original value input (quantitative variable)
X 4 Social support Original value input (quantitative variable)
X 5 Professional self‐concept Original value input (quantitative variable)
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a

Represent for different profiles of SWB.

TABLE 4.

Ordinal logistic regression analysis of factors associated with the latent profiles of SWB among nurses.

Variable B SE Wald χ 2 p OR 95% CI
Lower bound Upper bound
Age −0.02 0.02 1 0.32 0.98 −0.07 0.02
Titlea
Senior nurse 0.16 0.23 0.48 0.49 1.17 −0.13 0.79
Nurse in charge 0.37 0.28 1.79 0.18 1.45 −0.42 0.66
Head nurse and above 0.04 0.36 0.01 0.92 1.04 −0.74 0.66
Working yearsb
6–10 years −0.06 0.24 0.06 0.8 0.94 −0.85 0.21
11–15 years −0.01 0.3 0.002 0.97 0.99 −0.98 0.23
16–20 years 0.57 0.38 2.19 0.14 1.77 −1.68 −0.22
21–25 years 0.62 0.45 1.92 0.17 1.86 −1.92 −0.08
≥26 years 0.94 0.56 2.87 0.09 2.56 −2.03 0.15
Social support 0.04 0.005 73.89 <0.001 1.04 0.03 0.05
Professional self‐concept 0.01 0.001 51.48 <0.001 1.01 0.01 0.01
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Abbreviation: SE, standard error.

a

The title of ‘Nurse’ as the reference group.

b

Working years ‘≤5 years’ as the reference group.

5. DISCUSSION

This study is the first to use LPA to categorize different SWB profiles of nurses and their associated factors in China. It provides a new perspective by exploring the demographic distribution characteristics of nurses with different SWB profiles and proves that social support and professional self‐concept influence different categories of well‐being. As shown in Table 4, social support and professional self‐concept were associated with the different profiles of nurses’ SWB. These results also corroborated the content of PERMA theory and provided a theoretical basis for its application in nursing practice. However, this study only explored the effects of social support and professional self‐concept on SWB among a small number of nurses in the Guangdong Province of China. As such, the conclusions drawn from this study are not generalizable and should be interpreted with caution.

5.1. Characteristics of latent profiles of nurses’ SWB

The results showed individual differences in nurses’ SWB, which can be categorized into four latent profiles: Extremely Low SWB, Low SWB, Moderate SWB and High SWB. Among them, the mean score of Extremely Low SWB was (6.74 ± 1.91), Low SWB was (8.90 ± 1.01), Moderate SWB was (10.93 ± 1.26) and High SWB was (12.30 ± 1.97). Overall, this study showed that the average SWB score is moderate and above, indicating that the overall level of nurses’ SWB is moderate to high. However, a previous study by Yu et al. (2019) in 2018 surveyed 606 female nurses in the Liaoning Province region of China and found that the average well‐being score was low (8.27 ± 2.64). The findings of this study may be influenced by various factors such as the time of the survey, geographical location and characteristics of the nurse group. For example, the important role of nurses in fighting the COVID‐19 pandemic (WHO, 2020) and the significant increase in the importance of nursing staff to the public may have improved the SWB of nurses. In general, it also suggests that we should pay attention to nurses’ SWB.

5.2. Demographic characteristics of nurses’ latent profiles of SWB

According to the results of the ordered logistic regression, there is no reason for this study to consider title, working years and age as influencing factors of nurses’ SWB. However, the results of the univariate analysis showed differences in the distribution and characteristics of different categories of well‐being among nurses with different titles, working years and ages (p < 0.05). In this survey, 53.0% of the nurses had the title of ‘Nurse in charge’ or above. In categories of SWB ranging from Extremely Low to High, the percentage of nurses holding the title ‘Nurse in charge’ or above varied: 10.7% in Extremely Low SWB (57/535), 23.9% in Low SWB  (128/535), 31.8% in Moderate SWB (170/535), and 33.6% in High SWB (180/535). The majority of nurses with higher titles tend to have higher levels of well‐being. Having a higher title, to some extent, reflects an individual’s level of competence and a certain level of achievement (Seguin, 2019), thus influencing their SWB. The total number of nurses with Extremely Low SWB was 11.79% (119/1009), Low SWB was 28.54% (288/1009), Moderate SWB was 30.62% (309/1009) and High SWB was 29.04% (293/1009), respectively. As shown in Table 2, the number of nurses with Extremely Low SWB and Low SWB with more than 21 years of experience (79 in total) was relatively small compared to the number with Moderate SWB and High SWB (175 in total). Nurses with more years of experience (and thus confidence) tended to have higher SWB (Kim et al., 2023). Nurses in the High SWB group were older than those in the Extremely Low SWB, Low SWB and Moderate SWB groups (F = 8.08, p < 0.001). This indicates that senior nurses tend to have more professional clinical experience, are more skilled and comfortable in nursing and may enjoy status and respect in the nursing field, which, in combination, can lead to higher SWB compared to the rest of the population.

5.3. Factors associated with different profiles of nurses’ SWB

5.3.1. Social support as a factor influencing the latent profile of nurses’ SWB

The univariate analyses showed a difference in the distribution of social support between different SWB categories (p < 0.01). The highest social support was found for those with High SWB, followed by those with Moderate SWB, Low SWB and Extremely Low SWB. Previous studies have also confirmed the correlation between social support and SWB (Adriaenssens et al., 2017; Ersin et al., 2022; Schneider & Weigl, 2018). The results show that social support is a factor that influences the latent profiles of SWB (B = 0.04, p < 0.01); that is, social support predicts SWB and as social support increases, levels of SWB also increase. The possible reasons for this phenomenon are that social support is a functional component of relationships, including the provision of emotional support, information support, material support, among others and that the promotion and maintenance of positive interpersonal relationships can enable individuals to feel understood, accepted and cared for in their social relationships, reduce the negative impact of negative emotions on individuals and thus improve the mental health (Huang et al., 2023; Lu et al., 2023).

5.3.2. Professional self‐concept as a factor influencing the latent profile of nurses’ SWB

Our study demonstrated that nurses’ with a higher professional self‐concept were more likely to report a higher level SWB. This suggests that the higher the nurses’ professional self‐concept, the higher their SWB, which concurs with the conclusions of previous studies (Céspedes et al., 2020; Cowin et al., 2008). This may be owing to the fact that nurses’ increased self‐identification with their roles and sense of professional worth can enable them to feel autonomous and fulfilled in the realization of their aspirations and goals and can therefore contribute to their SWB (Ren et al., 2021). Therefore, improving nurses’ professional self‐concept plays an important role in their SWB.

5.4. Implications for the profession and practice

To promote nurses’ SWB, nursing managers can provide targeted support according to the characteristics of the different latent profiles of SWB (Stevanin et al., 2018). For nurses with extremely low SWB or low SWB, nursing managers should not only understand their current difficulties and needs, but also comprehensively analyse their level of social support and professional self‐concept. Targeted interventions and supports can then be developed and provided to improve their overall well‐being. Moderately SWB nurses should maintain their present level of well‐being and work towards improving it. Additionally, nurses at a low level of well‐being should learn methods to enhance and maintain a high level of well‐being from nurses who are already at a high level. Actions include establishing a supportive work environment, encouraging cooperation among teams, providing psychological counselling services, arranging the transfer of shifts within reason and helping different nurses align their career planning and work assignments with their needs and abilities. Additionally, nursing managers can guide nurses in planning their career development by strengthening their professional education, developing targeted training programmes and providing role models (Li et al., 2021). Furthermore, they should pay attention to the development of nurses’ professional self‐concept, provide guidance and training for nurses at different career stages, provide equal and fair development opportunities and motivate them to enhance their self‐value and satisfaction (Ren et al., 2021).

5.5. Limitations

This study has some limitations. First, we only selected nurses from public hospitals in Guangdong Province with the use of a convenient sampling method. A more rigorous approach such as random sampling or stratified sampling design should be considered in the future. Second, as this was a cross‐sectional study, we were not able to determine a causal relationship. Thus, it is necessary to conduct surveys in multiple regions and longitudinal studies to include more variables. Third, the online questionnaire was self‐reported and therefore may contain bias. Future researchers could supplement the survey with qualitative data from face‐to‐face interviews and use different measurement tools or analysis methods such as machine learning algorithms, to explore the influencing factors of SWB to validate and develop the findings of this study. A final limitation of this study is that it only considered demographics, social support and professional self‐concept as factors that influence SWB. Future studies should include more relevant variables.

6. CONCLUSION

In this study, nurses’ SWB was classified into four profiles through the LPA with a person‐centred approach. While 59.60% of nurses in this study were classified as having moderate SWB or high SWB, another 40.34% were classified as having extremely low SWB or low SWB. Nurses’ lower SWB needs to be further enhanced by improving nurses’ social support and professional self‐concept. Schools should establish and develop nurses’ self‐concept at an early stage; nursing managers should provide different levels of support to nurses according to their personal characteristics, as well as ongoing professional training to promote their self‐concept, thereby enhancing nurses’ overall well‐being and career development.

AUTHOR CONTRIBUTIONS

CYM: study design, data collection, data cleaning, data analysis, writing – original draft preparation, and writing – review and editing. CQL: data analysis and writing – review and editing. YZ: study design, data collection, supervision, and writing – guidance. JWYC: study design and supervision. XFZ: data collection and writing – guidance. WYT: data collection, data cleaning and data checking. YM: data collection and data cleaning. QL: data analysis. JNC: writing – revising. TKSW: writing – guidance. YZ and JWYC should be considered as the co‐corresponding authors.

FUNDING INFORMATION

This research was funded by grants from: 1. The Key Project of Nursing Psychology Scientific Research Planning Subjects in 2022 by the Nursing Psychology Professional Committee of China Association for Mental Health (CAMH); 2. The Key Discipline Project (Nursing) of Guangzhou Education Bureau; 3. Research Innovation Project for Postgraduate Education in the School of Nursing, Guangzhou Medical University (HY202221).

CONFLICT OF INTEREST STATEMENT

None.

ETHICS STATEMENT

This study was approved by the Medical Ethics Committee of the Guangzhou Medical University (No. 202210003). This study is part of a larger investigation registered in the Chinese Clinical Trial Registry (ChiCTR2200066699).

Supporting information

Table S1.

NOP2-11-e2146-s001.docx (24.5KB, docx)

ACKNOWLEDGEMENTS

We extend our sincere gratitude to all the nurses who actively participated in this study and generously dedicated their valuable time.

Miao, C. , Liu, C. , Zhou, Y. , Chung, J. W. Y. , Zou, X. , Tan, W. , Ma, Y. , Luo, Q. , Chen, J. , & Wong, T. K. S. (2024). Latent profiles of nurses’ subjective well‐being and its association with social support and professional self‐concept. Nursing Open, 11, e2146. 10.1002/nop2.2146

Contributor Information

Ying Zhou, Email: zhouying0610@163.com.

Joanne W. Y. Chung, Email: joannechung@kwnc.edu.mo.

DATA AVAILABILITY STATEMENT

The data generated by this study are available from the corresponding author on reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1.

NOP2-11-e2146-s001.docx (24.5KB, docx)

Data Availability Statement

The data generated by this study are available from the corresponding author on reasonable request.

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