Global healthcare systems are increasingly being challenged by a critical shortage of nurses, which is further aggravated by suboptimal team performance and elevated turnover rates among nursing staff. Team resilience has emerged as a crucial protective factor that offers substantial benefits and the potential to improve adverse work outcomes. This study sought to elucidate the profiles of nurses’ team resilience and examine their relationship with team performance and turnover intention.
A total of 217 nursing teams comprising 1,618 nurses were recruited through stratified convenience sampling from five tertiary and five secondary hospitals in Shandong Province, China. Team resilience was evaluated using the Analyzing and Developing Adaptability and Performance in Teams to Enhance Resilience Scale, while team performance was measured using the Team Effectiveness Scale. Additionally, turnover intention was assessed using the self-administered question, “Have you thought of quitting the job?” Latent profile analysis was used to identify distinct profiles of team resilience. A regression analysis was conducted to examine the associations between team resilience profiles, team performance, and turnover intention.
Three latent profiles of team resilience among nurses were identified: worst (Class 1, 21.659%), mid-range (Class 3, 43.318%), and best (Class 2, 35.023%). Compared with Class 1, Classes 2 (beta = 0.922, p < 0.001; odds ratio [OR] = 0.154, p < 0.001) and 3 (beta = 0.463, p < 0.001; OR = 0.258, p < 0.01) exhibited significantly better team performance and lower turnover intention.
This study investigated the potential profiles of nurse team resilience, identifying subgroups characterized by underperformance and elevated turnover intention. Hospital administrative decision-makers and nursing managers should enhance investment in nursing resources and strategically allocate resources as well as tailor or optimize team interventions based on the heterogeneity observed in team resilience to foster positive changes in adverse work outcomes.
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Abkürzungen
ADAPTER
Analyzing and developing adaptability and performance in teams to enhance resilience scale
TES
Team effectiveness scale
rwg
Interrater agreement index
ICC
Intraclass correlation coefficient
LPA
Latent profile analysis
LMR
Lo-mendell-rubin adjusted likelihood ratio test
BLRT
Bootstrap likelihood ratio test
AIC
Akaike information criterion
BIC
Bayesian information criterion
ABIC
Sample-size-adjusted bayesian information criterion
Introduction
With the acceleration of the aging population and a concomitant increase in the demand for healthcare services, a shortage of nurses has emerged as a critical challenge for healthcare systems globally. A report by the International Council of Nurses (ICN) indicated a current global deficit of 13 million nurses, with projections suggesting further escalation of this shortfall [1]. In China, this situation is particularly acute, with severe nursing shortages. According to national statistics, the total number of registered nurses in China is 4,709,000, with an estimated shortfall of three million in terms of addressing the existing deficit [2‐4]. This persistent shortage of nursing staff adversely affects the performance and mental well-being of the current workforce because the resulting disproportionate nurse-to-patient ratio compels the remaining nurses to undertake increased workloads. Furthermore, this shortage raises significant concerns regarding patient safety and poses potential threats to patients’ lives, as exemplified by delays in nursing care and medication errors [5‐7]. Consequently, the suboptimal work performance of retained nurses, coupled with their elevated turnover rates, has garnered increasing scholarly attention [8]. Within this context, the issue of nurse shortages is anticipated to become increasingly pronounced.
As research on positive organizational behavior expands, scholars have increasingly highlighted the strengths and efficacy of teams in addressing human resource shortages, particularly in the nursing context. Team resilience—defined as the capacity of a team to adapt to and recover from stressful crises—serves as a protective factor that enhances the coping mechanisms and cohesion of work teams operating in high-pressure environments [9, 10]. This, in turn, significantly contributes to the physical and mental well-being of team members, sustains team performance, and fosters professional identity [11‐13]. For over 20 years, researchers have developed conceptual frameworks connecting team resilience and job outcomes [14, 15]. A substantial body of empirical research indicates a positive correlation, suggesting that increased levels of team resilience are associated with enhanced team performance and reduced turnover intentions among team members [16‐18]. However, there is a paucity of scientific evidence addressing heterogeneity in team resilience among nurses and its consequent effects on team performance and turnover intentions. Research indicates that a team’s resilience is frequently influenced by a multitude of factors [19]. Variations in work environment, team size, and diverse backgrounds of team members, particularly within nursing teams, often lead to differing levels of responsiveness and adaptability when confronted with stressful challenges. Acknowledging the heterogeneity of team resilience can facilitate the strategic allocation of resources and the optimization of team coping strategies. Furthermore, tailored interventions that address this heterogeneity are more efficacious and cost-effective than interventions that do not consider such diversity [20]. To address this gap in existing evidence, we adopted a comprehensive approach to investigate the resilience of nursing teams and its correlation with work outcomes.
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Latent profile analysis (LPA) is a human-centered statistical technique that offers an alternative to traditional methods that focus on surface-level variable scores [21]. Unlike conventional approaches, LPA identifies heterogeneity within a sample by mapping the score trajectories of variables across multiple dimensions. This allows for the observation and characterization of distinct category profiles, facilitating the grouping of samples based on their different characteristics [22]. Considering the established relationship between nurses’ work performance, turnover intentions, and team resilience and with awareness of the lack of compelling evidence available to managers and administrators regarding the effectiveness of resilience compared with conventional alternatives, this study aimed to transcend traditional analytical frameworks that treat team resilience as an independent variable. Instead, it sought to identify the prevalent profiles of nurse team resilience and examine the correlations between these profiles and work outcomes using a person-centered approach. We hypothesized that there would be heterogeneity in the level of resilience among different nursing teams. The approach adopted in this study is expected to advance the literature, with findings that can provide a realistic basis for nursing managers to tailor relevant interventions to effectively address adverse work outcomes among nurse teams and reduce nursing staff turnover.
This study had two objectives: i) to identify and characterize the potential profiles of resilience within nurse teams; and ii) to utilize regression analysis to investigate the relationship between these distinct resilience profiles and the performance of nursing teams and nurses’ turnover intentions.
Methods
Study design, setting, and participants
This was a cross-sectional study of nursing teams (comprising multiple nurses operating within the same department or ward). To ensure sample heterogeneity, stratified convenience sampling was employed to sample hospitals with separate levels of medical service (Grades 2 and 3) in Shandong Province, China. The inclusion criteria consisted of practicing nurses who held a People’s Republic of China nursing qualification certificate and who worked in clinical nursing at a hospital. Exclusion criteria encompassed nurses who were not engaged in bedside care.
Data sources
As part of the Longitudinal Nurses’ Health Study, we have collected annual sample data in Shandong Province since 2020. For this study, we included 217 nursing teams, comprising 1618 nurses, from five tertiary and five secondary hospitals in Shandong Province between 2021 and 2022. Each team comprised between 5 and 13 members. Prior to the commencement of data collection, we secured support from the nursing departments of each participating hospital to facilitate the smooth progression of the study. During the data collection phase, for departments or wards exhibiting low response rates, the authors sent reminders to encourage participation by sending emails or utilizing the WeChat workgroups (Tencent Holdings Limited) of the respective departments or wards, as provided by the nursing departments. In this study, the effective response rate for each team unit ranged from 62.5% to 100%, with an overall effective response rate of 86.2%, and no data were missing.
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Ethical considerations.
This study was approved by the Ethics Committee of Scientific Research of Shandong University School of Nursing and Rehabilitation (No. 2021-R-131). To ensure voluntary participation and data integrity during the data collection phase, several measures were implemented. Participants were provided with a comprehensive informed consent form, which clearly outlined the study’s purpose, procedures, potential risks, and benefits, while emphasizing the voluntary nature of their involvement. Anonymity was maintained by employing data collection methods that avoided the acquisition of personally identifiable information. Furthermore, all personnel involved in data collection underwent extensive ethical training to ensure strict adherence to ethical guidelines, thereby safeguarding the autonomy of participants and the accuracy of their responses. The study prioritized confidentiality and compliance with the Declaration of Helsinki. As this study did not involve a clinical trial or any interventional procedures, a clinical trial number is not applicable.
Measurement
Sociodemographic characteristics
Data were obtained concerning the following sociodemographic characteristics: gender, age, marital status, education level, number of children, professional title, satisfaction with salary, position, years working in the current ward, and fringe benefits (such as parental leave, insurance, and retirement pensions). To obtain information on the sociodemographic characteristics of the nursing teams, we aggregated the individual nurses’ sociodemographic information (Supplementary Table 1).
Team characteristics
Team characteristics included team size, team maturity (i.e., head nurse’s working years in the current ward), team type (such as internal or surgical), and hospital level (Grades 2 and 3).
The Analyzing and Developing Adaptability and Performance in Teams to Enhance Resilience Scale (ADAPTER)
The ADAPTER [10, 23] was used to measure team resilience among the nurse teams. It comprises 51 items divided into seven dimensions: responding, shared transformational leadership, learning, anticipating, monitoring, cooperation with other departments, and heedful interrelating. Each item is scored on a 5-point Likert scale, with answers ranging from 1 (strongly disagree) to 5 (strongly agree). The higher the total score, the greater the team’s resilience. In the present study, a confirmatory factor analysis was conducted on the scale, revealing an adequate model fit (χ2 = 7544.04, df = 1182, χ2/df = 6.38, CFI = 0.906, RMSEA = 0.08, SRMR = 0.02). The Cronbach’s alpha coefficient for the ADAPTER in this sample was 0.993, signifying high internal consistency.
The Team Effectiveness Scale (TES)
Team performance was evaluated using the TES for two dimensions (task performance and cooperation satisfaction), following Tjosvold [24]. It comprises eight items answered on a 5-point Likert scale, with answers ranging from 1 (strongly disagree) to 5 (strongly agree). Higher total scores indicate better team performance. Confirmatory factor analysis conducted on this sample demonstrated an acceptable model fit (χ2 = 34.582, df = 12, χ2/df = 2.88, CFI = 0.997, RMSEA = 0.05, SRMR = 0.01). The internal consistency of the TES, as indicated by Cronbach’s alpha, was 0.972.
Turnover intention
Turnover intention was assessed using a single item question asking nurses to rate their overall intention to leave the current workplace: “Have you thought of quitting the job?” A 4-point scale was employed to measure responses, classifying nurses as either “yes” (almost always or often) or “no” (occasionally or never).
Statistical analysis
For continuous variables that conformed to a normal distribution, means and standard deviations were used for descriptive purposes. For data that deviated from a normal distribution, the median and interquartile range were used. Categorical variables are characterized using frequencies and percentages. Statistical analyses were conducted using Excel, SPSS version 25.0, and Mplus version 8.3 software packages.
Aggregation analysis
Given that our study focused on teams and that team resilience and performance represent shared perceptions of attitudes and beliefs among team members, we aggregated individual nurse scores to the team level (i.e., averaged team member scores were calculated for each team). To assess the appropriateness of aggregating data at the team level for analysis, evaluations were conducted on inter-rater agreement (rwg, acceptable range > 0.7), intraclass correlation (ICC), and reliability in relation to group means (ICC 1, acceptable range > 0.05; ICC 2, acceptable range > 0.5) [25, 26]. The rwg values for team resilience and performance ranged from 0.88 to 0.96, while the ICC (1) and ICC (2) values were between 0.06 to 0.1 and 0.5 to 0.6, respectively. These metrics indicate that the data were suitably aggregated at the team level (Supplementary Table 2). To analyze turnover intentions at the team level while accounting for variations in team size, we tailored the quantification of response categories for members within each team. Specifically, a team was deemed to exhibit turnover intention if the proportion of responses in the categories “almost always” and “often” exceeded those in the categories “occasionally” and “never.” Conversely, if responses in the “occasionally” and “never” categories were more prevalent, the team was not considered to demonstrate turnover intention.
LPA
Utilizing a defined set of factors or variables, LPA facilitates the identification of potential subgroups within a population that exhibit similar response patterns [27]. In order to achieve the research objectives, we identified potential profiles of nurse team resilience by examining the seven dimensions of the ADAPTER framework. The number of profiles in the model was increased incrementally from the initial stage until the optimal model was determined. All models were collectively assessed using LPA model fit indices, including the Lo-Mendell-Rubin (LMR) adjusted likelihood ratio test, bootstrap likelihood ratio test (BLRT), entropy, Akaike information criterion (AIC), Bayesian information criterion (BIC), and the sample-size-adjusted BIC (ABIC). The criteria for determining a good model fit were low AIC, BIC, and ABIC values, significant p-values for the LMR and BLRT, and entropy values approaching 1 [28, 29]. Furthermore, each identified subtype had to represent at least 5% of the total sample to prevent over-stratification [30, 31].
Univariate analysis
Welch’s analysis of variance (ANOVA) was conducted to analyze the relationship between nurses’ team resilience profiles and team performance by employing the Games-Howell test for post-hoc multiple comparisons. Additionally, a chi-square test was used to examine the association between team resilience profiles and turnover intention, with Bonferroni corrections applied for post-hoc multiple comparisons.
An independent-samples t-test, one-way ANOVA, Pearson correlation analysis, and chi-square test were used to analyze variations in team resilience profiles, team performance, and turnover intention among nursing teams with differing sociodemographic and team characteristics.
Regression analysis
Logistic and multiple linear regression models were employed to examine the association between latent profiles of team resilience and team performance, as well as turnover intention. Statistically significant demographic and team variables identified in the univariate analysis were included as covariates in the model.
Results
Latent profiles of team resilience among nurses
As depicted in Table 1, the 2-profile model exhibited higher AIC, BIC, and ABIC values than the other models, suggesting poorer fitness and parsimony. The 4-profile model exhibited a nonsignificant p-value for the LMR test, indicating that it did not offer a superior fit relative to the 3-profile model. Although the 5-profile model exhibited excellent fit metrics, its second subcategory comprised less than 5% of the total sample, which was a limitation. Consequently, when considering all the fit metrics collectively, the 3-profile model emerged as the optimal choice.
Table 1
Model Fit indices for latent profile analysis of team resilience (N = 217)
No. of profiles
Proportion
AIC
BIC
ABIC
Entropy
p for LMR
p for BLRT
1
217
6480.423
6527.742
6483.377
-
-
-
2
77(0.355)/140(0.645)
5314.451
5388.809
5319.094
0.944
0.0789
< 0.001
3
47(0.217)/76(0.350)/94(0.433)
4595.084
4696.481
4601.415
0.966
0.0368
< 0.001
4
9(0.041)/48(0.221)/89(0.410)/71(0.327)
4213.584
4342.020
4221.603
0.969
0.2180
< 0.001
5
40(0.184)/9(0.041)/67(0.309)/62(0.286)/39(0.180)
3901.308
4056.783
3911.015
0.968
0.0324
< 0.001
AIC, akaike information criterion, BIC bayesian information criterion, ABIC sample-size-adjusted bayesian information criterion, LMR lo-mendell-rubin adjusted likelihood ratio test, BLRT bootstrap likelihood ratio test
Figure 1 illustrates that the 3-profile model of team resilience exhibited a comparable distribution across the subtypes. Specifically, Class 1, designated as the “worst team resilience” subtype, exhibited the lowest level of team resilience. Conversely, Class 2, identified as the “best team resilience” subtype, exhibited the highest level of team resilience, while Class 3, termed the “mid-range team resilience” subtype, exhibited an intermediate level of team resilience. Furthermore, nursing teams consistently scored relatively low across the three competencies of learning, anticipation, and heedful interrelating, regardless of subtype.
Fig. 1
Latent profiles of team resilience among nurses. Note. R, responding; S, shared transformational leadership; L, learning; A, anticipating; M, monitoring; C, cooperation with other departments; H, heedful interrelating; The vertical axis represents the score for each dimension of team resilience
×
Characteristic differences of team resilience across three profiles
The results of the univariate analysis indicated statistically significant differences among profiles with respect to gender, educational level, salary satisfaction, team maturity, and hospital level (all p < 0.05; see Supplementary Table 3).
Differences in team performance and turnover intention by profile
As illustrated in Table 2, there were significant differences in the dimensions and overall team performance scores across the potential profiles of team resilience among the nurses (p < 0.001). Post-hoc analyses revealed that the group with the lowest team resilience (Class 1) exhibited the lowest scores in both dimensions and overall team performance, whereas the group with the highest team resilience (Class 2) exhibited the highest scores in these areas. Furthermore, turnover intention exhibited significant differences across the potential profiles of team resilience among nurses (p < 0.001). Subsequent post-hoc analysis revealed that the group with the lowest level of team resilience (Class 1) exhibited the highest turnover intention.
Table 2
Disparity between latent profiles of team resilience and team performance, turnover intention among nurses (N = 217)
Variables
Total sample (n = 217)
Class 1 (n = 47)
Class 2 (n = 76)
Class 3 (n = 94)
F/χ2
p
Team performance
35.85 (2.33)
33.17 (1.92)c
38.03 (1.10)a
35.44 (1.46)b
167.86
< 0.001
Task performance
22.25 (1.51)
20.57 (1.24)c
23.63 (0.76)a
21.97 (0.99)b
150.64
< 0.001
Cooperation satisfaction
13.60 (0.88)
12.60 (0.75)c
14.39 (0.40)a
13.47 (0.58)b
154.06
< 0.001
Turnover intention
25.625
< 0.001
Yes
49 (100%)
23 (46.9%)e
8 (16.3%)f
18 (36.7%)f
No
168 (100%)
24 (14.3%)e
68 (40.5%)f
76 (45.2%)f
Note. Class 1, “worst team resilience” subtype; Class 3, “mid-range team resilience” subtype; Class 2, “best team resilience” subtype
a, Highest scoring group in the post-hoc analysis (p < 0.001). b, Medium scoring group in the post-hoc analysis (p < 0.001). c, Lowest scoring group in the post-hoc analysis (p < 0.001); e and f, Same letter represents no statistical difference between groups
Differences in team performance and turnover intentions among nurse teams with various sociodemographic and team characteristics
As presented in Table 3, the findings of the univariate analysis indicated that gender, education level, salary satisfaction, and hospital level exhibited statistically significant correlations with team performance (p < 0.05). Additionally, gender, salary satisfaction, and team type were significantly correlated with turnover intention (p < 0.05).
Table 3
Characteristic differences of team performance and turnover intention among nurses (N = 217)
Characteristics
Team performance
t/F/r
p
Turnover intention
t/χ2
p
Yes
No
Gender
−2.492
0.013*
6.533
0.011*
Teams without men
35.18 (2.63)
19 (38.8%)
35 (20.8%)
Teams with men
36.08 (2.18)
30 (61.2%)
133 (79.2%)
Age
−0.091
0.182
31.78 (3.00)
32.20 (2.90)
−0.887
0.376
Educational level
−2.273
0.024*
1.434
0.231
Team without postgraduate
34.87 (2.47)
8 (16.3%)
17 (10.1%)
Team with postgraduate
35.98 (2.29)
41 (83.7%)
151 (89.9%)
Marital status
−0.032
0.639
2.12 (1.84)
1.82 (1.52)
1.182
0.239
No. of children
0.009
0.891
1.03 (0.44)
1.07 (0.42)
−0.642
0.522
Satisfaction with salary
−5.031
< 0.001***
5.075
0.024*
Team with dissatisfied staff
35.12 (2.30)
32 (65.3%)
79 (47.0%)
Team without dissatisfied staff
36.63 (2.10)
17 (34.7%)
89 (53.0%)
Professional title
−0.098
0.149
2.88 (1.81)
3.03 (1.68)
−0.548
0.584
Position
−0.099
0.145
1.24 (0.48)
1.14 (0.37)
1.375
0.174
Fringe benefits
−0.108
0.112
1.51 (1.26)
1.67 (1.28)
−0.758
0.449
Years working in the current ward
−0.077
0.262
7.06 (2.79)
7.26 (3.25)
−0.388
0.698
Team size
−0.061
0.369
7.57 (1.94)
7.42 (1.83)
0.494
0.622
Team type
1.748
0.111
15.399
0.017*
Internal
35.96 (2.29)
7 (14.3%)
38 (22.6%)
Surgery
36.41 (2.35)
9 (18.4%)
47 (28.0%)
Obstetrics and gynecology
35.64 (2.47)
4 (8.2%)
28 (16.7%)
Pediatrics
35.83 (2.14)
6 (12.2%)
19 (11.3%)
Emergency
35.67 (1.91)
8 (16.3%)
16 (9.5%)
Operating room
32.77 (3.41)
2 (4.1%)
1 (0.6%)
Critical care
35.38 (2.39)
13 (26.5%)
19 (11.3%)
Team maturity
0.016
0.816
22.43 (20.13)
22.45 (19.34)
−0.006
0.995
Hospital level
−4.643
< 0.001***
0.431
0.512
3 grades hospital
36.66 (2.17)
23 (46.9%)
70 (41.7%)
2 grades hospital
35.25 (2.27)
26 (53.1%)
98 (58.3%)
Note. Age, average age of team; Marital status, number of unmarried nurses in team; No. of children, average number of children in team; Professional title, number of nurses with intermediate titles and above in team; Position, number of head nurses in team; Fringe benefits, number of satisfied respondents in team; Years working in the current ward, average working years of team
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Relationship between latent profiles of team resilience and team performance, as well as turnover intention
Multiple linear regression analyses indicated that teams classified under the “mid-range team resilience” subtype (Class 3, Beta = 0.463, p < 0.001) and the “best team resilience” subtype (Class 2, Beta = 0.922, p < 0.001) demonstrated superior performance relative to those in the “worst team resilience” subtype (Class 1). These findings were obtained after controlling for variables such as gender, education level, satisfaction with salary, and hospital level (Table 4). Furthermore, logistic regression analyses indicated that, after adjusting for gender, satisfaction with salary, and team type, nurse teams classified within the “mid-range team resilience” subtype (Class 3) exhibited a 74.2% lower likelihood of turnover intention compared with those in the “worst team resilience” subtype (Class 1) (odds ratio [OR] = 0.258, p < 0.01). By contrast, nursing teams within the “best team resilience” subtype (Class 2) exhibited an 84.6% lower likelihood of turnover intention (OR = 0.154, p < 0.001) (Table 5).
Table 4
Regression of team performance on team resilience profile (N = 217)
Variables
B
SE
Beta
t
p
95% CI
Team performance
“Mid-range team resilience” subtype
2.172
0.262
0.463
8.297
< 0.001
(1.656, 2.688)
“Best team resilience” subtype
4.495
0.291
0.922
15.468
< 0.001
(3.922, 5.068)
Gender (ref: teams without men)
Teams with men
0.250
0.232
0.046
1.078
0.282
(−0.207, 0.707)
Educational level (ref: team without postgraduate)
Team with postgraduate
−0.138
0.318
−0.019
−0.434
0.665
(−0.766, 0.489)
Satisfaction with salary (ref: team with dissatisfied staff)
Team without dissatisfied staff
0.546
0.206
0.117
2.647
0.009
(0.139, 0.952)
Hospital level (ref: 2 grades hospital)
3 grades hospital
0.433
0.209
0.092
2.071
0.040
(0.021, 0.846)
Reference subtype: “Worst team resilience” subtype (class 1); “Best team resilience” subtype, class 2; “Mid-range team resilience” subtype, class 3; B, unstandardized coefficients, SE standard error, Beta standardized coefficients, CI confidence interval, Gender, educational level, satisfaction with salary and hospital level were controlled as covariates in this model
Table 5
Regression of turnover intention on team resilience profile (N = 217)
Variables
B
SE
OR
p
95% CI
Turnover intention
“Mid-range team resilience” subtype
−1.353
0.415
0.258
0.001
(0.114, 0.583)
“Best team resilience” subtype
−1.873
0.508
0.154
< 0.001
(0.057, 0.416)
Gender (ref: teams without men)
Teams with men
0.211
0.529
1.235
0.689
(0.438, 3.482)
Satisfaction with salary (ref: team without dissatisfied staff)
Team with dissatisfied staff
0.522
0.386
1.685
0.176
(0.791, 3.591)
Team type (ref: internal)
Surgery
0.254
0.585
1.290
0.664
(0.410, 4.057)
Obstetrics and gynecology
−0.171
0.705
0.843
0.808
(0.212, 3.354)
Pediatrics
0.612
0.659
1.843
0.354
(0.506, 6.713)
Emergency
0.900
0.695
2.459
0.195
(0.630, 9.595)
Operating room
1.734
1.410
5.661
0.219
(0.357, 89.834)
Critical care
1.554
0.683
4.730
0.023
(1.239, 18.052)
Reference subtype: “Worst team resilience” subtype (class 1); “Best team resilience” subtype, class 2; “Mid-range team resilience” subtype, class 3; B, unstandardized coefficients, SE standard error, OR odds ratio, CI confidence interval, Gender, satisfaction with salary and team type were controlled as covariates in this model
Discussion
This study aimed to examine and evaluate the relationships among team resilience, team performance, and turnover intentions at team levels among nurses within a heterogeneous sample of hospitals in China. While this study concentrated on a Chinese cohort, the issue of work-related stress among nurses has global significance. Resilience has been widely recognized across various countries as being linked to enhanced performance and the mitigation of human resource shortages, thereby underscoring the broader applicability and value of this research. This study delineated three distinct subtypes in terms of nurse team resilience: “worst team resilience” (Class 1), “mid-range team resilience” (Class 3), and “best team resilience” (Class 2). A dose–response relationship was observed between the identified profiles of nurse team resilience and outcomes related to team performance and turnover intentions. Specifically, groups exhibiting higher levels of team resilience demonstrated superior team performance and reduced turnover intentions. These findings were established while controlling for covariates, including sociodemographic and team characteristics.
Through identifying these specific profiles, this study offers practical, evidence-based findings to help inform hospital administrative decision-making and clinical nursing management. We hypothesized that there would be heterogeneity in the level of resilience among different nursing teams, which was substantiated in this study’s findings. Specifically, 21.659% of the nursing teams were at the worst level of team resilience (Class 1), 35.023% were at the highest level of team resilience (Class 2), and the remaining 43.318% demonstrated a moderate level of team resilience (Class 3). This heterogeneity may be associated with sample characteristics such as gender, education level, salary satisfaction, team maturity, and hospital level. To the best of our knowledge, this finding has not been previously reported and may be explained through our application of a more comprehensive approach than the conventional method of examining the effects of team resilience in isolation [17, 32, 33]. Further investigation is warranted to explore this possibility.
The findings from the regression analyses demonstrated a significant enhancement in nurses’ team performance and a reduction in turnover intention in profiles characterized by the best team resilience. In other words, when managers actively foster higher levels of team resilience among nurses, there is likely to encourage a positive shift in job-related outcomes, as identified in our study. We conducted a comparative analysis of our findings with those of prior studies that examined the relationship between team resilience and performance, which showed that our results accorded with those reported in those studies. Hartwig et al. [15] conducted a systematic review and conceptual analysis of team resilience in the workplace and explored its antecedents and outcomes. They concluded that team resilience serves as a valuable asset, enabling work teams to sustain their performance under adverse conditions. Bowers et al. [14] conceptualized team resilience as a second-order emergent state, suggesting that it enables teams to sustain or restore their performance levels when confronted with adversity. However, their work did not include an empirical investigation; rather, their conclusions were obtained after evaluating a series of proposed research hypotheses derived from theoretical modeling. In contrast, we empirically tested our hypothesis in this study, demonstrating that teams with the best profiles (Class 2) were more likely to exhibit superior performance than those with the worst profiles (Class 1).
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Son and Ham [17] investigated the association between team resilience and job-related outcomes, specifically job satisfaction and turnover intentions, among nursing staff across five hospitals located in both provincial and metropolitan areas of South Korea. Through individually modeling the team resilience of nurses, they found that increased levels of team resilience were positively correlated with enhanced job satisfaction and inversely related to turnover intention. Analogous outcomes were noted for job performance. In our study, these relationships were significantly more pronounced, with the best profile correlating with an 84.6% reduction in turnover intention among the nursing teams compared with the worst profile. According to resource theory, resilience is contingent on a diverse array of resources that can be mobilized by a team in stressful situations [34‐36]. These resources encompass personal factors such as stress management skills, organizational factors such as support from healthcare institutions, and social factors such as social networks. Collectively, these resources function as buffers and protect against excessive stress and adverse circumstances. Therefore, enhancing team resilience among nurses is crucial for fostering their ability to develop and effectively utilize an array of resources and capacities when confronted with adverse situations [37]. Moreover, in circumstances wherein resource depletion is inevitable, a higher level of team resilience enables nursing teams to manage these losses more effectively by reallocating the remaining resources to prioritized activities and generating new resources and capacities. Consequently, initiatives aimed at improving team performance and reducing turnover intentions should incorporate strategies to strengthen nurses’ team resilience.
Limitations
This study employed a cross-sectional design, which poses challenges in terms of inferring causal relationships without the use of prospective longitudinal surveys. Future research aimed at clarifying the dynamic relationship between nurse team resilience profiles and outcomes, such as team performance and turnover intentions, is essential. LPA assigns individuals based on their highest probability of belonging to one of the profiles but not necessarily to a single group. Consequently, careful consideration is required when interpreting these results. We did not evaluate work environment factors and organizational issues that could have influenced nurses’ team performance and turnover intentions, although we accounted for demographic and team-related variables. Furthermore, this study utilized the pre-existing dimensional framework of the ADAPTER scale to identify and characterize potential profiles of nurse team resilience. However, this framework may not have sufficiently captured other potentially critical aspects of team resilience, such as the team’s capacity for innovation and resource integration. This limitation may have contributed to the observed similarity in the distribution of team resilience profiles, indicating a lack of clear distinction among the dimensions. Future research should aim to broaden the conceptualization of team resilience and develop a more comprehensive measurement system to enhance the validation of our findings.
Conclusion
Given the significance of resilience in enhancing the quality of work among nursing teams and in addressing the shortage of nursing human resources, there is an urgent need for empirical evidence to assist nurse leaders with suboptimal team resilience profiles in customizing interventions and advocate that hospital administrators increase investment in nursing resources. This study offers evidence-based findings through applying LPA, demonstrating that nursing teams with the highest resilience profiles exhibit superior performance to those with the lowest resilience profiles. Furthermore, elevated levels of team resilience were identified as predictors of reduced turnover intention among nurses.
Clinical implications for nursing management
For decades, nursing management has concentrated on reconciling the increasing demand for care with the provision of high-quality services. Addressing this challenge requires a thorough understanding of the fundamental causes underlying the underperformance of nursing teams and their human resource deficiencies. It is imperative that nurse managers and hospital administrators prioritize teams that exhibit lower resilience. Based on the findings of this study, which identified low competencies in terms of learning, anticipation, and heedful interrelating of nursing teams as primary weaknesses in team resilience, the following measures may warrant consideration for investment.
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a)
To enhance learning competence, it is imperative that nurse managers or leaders in nursing departments actively engage in educational activities, thereby underscoring the significance of continuous knowledge acquisition and professional development. Leaders should participate in academic conferences and seminars and subsequently disseminate their acquired knowledge and insights during team meetings to motivate and inspire nurses toward lifelong learning. It is also advisable to establish regular knowledge-sharing sessions, such as nursing case discussions, to provide nurses with the opportunity to exchange experiences, share learning achievements, and address challenges encountered in their professional practice. Implementing an incentive mechanism to acknowledge and reward nursing teams that demonstrate active engagement in learning and professional development is recommended.
b)
Enhancing training in emergency management is crucial. It is essential for nursing teams to be well-versed in protocols to address a range of emergencies, including cardiac arrest and respiratory distress. Greater familiarity in relation to these situations would enable them to respond swiftly and effectively when such situations arise, to anticipate potential emergencies, and to prepare proactively. For instance, conducting first-aid drills that simulate cardiac arrest scenarios can help nurses become adept at the first-aid procedures and the collaborative efforts required during these drills, thereby improving their competence in managing and predicting emergencies.
c)
Concerning the competence of nursing teams in establishing more effective heedful interrelations and specifically in terms of greater intra-team cooperation efficacy, management should implement specialized training programs focused on teamwork. These programs should include modules for communication skills, conflict resolution, and role recognition. Role-playing exercises can be used to simulate potential communication challenges and conflict situations that may occur in the workplace, followed by collaborative discussions aimed at devising effective solutions to these issues. Managers can enhance a team’s intrinsic effectiveness by strategically configuring its structure through the recruitment and selection of nurses with diverse personality traits and specialties.
Acknowledgements
We acknowledge the Science and Technology Agency of Shandong Province for the financial support and help in conducting the study. Moreover, we would like to thank the nursing department of ten hospitals and participants for their support to this study.
Declarations
Ethics approval and consent to participate
This study received approval from the Ethics Committee of Scientific Research at Shandong University School of Nursing and Rehabilitation (Ethics Approval No. 2021-R-131). All subjects signed an informed consent form and voluntarily completed the questionnaires. Furthermore, all the information obtained from the participants remained strictly confidential and anonymous. All the methods of the study were carried out in accordance with relevant guidelines and regulations. This study did not involve a clinical trial or any interventional procedures; therefore, a clinical trial number is not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
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