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BMC Nurs
BMC Nurs
BMC Nursing
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BioMed Central London

39267008
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10.1186/s12912-024-02313-0
Research
Effects of job demands, job resources, personal resources on night-shift alertness of ICU shift nurses: a cross‑sectional survey study based on the job demands-resources model
Gou Jiayan 1
Zhang Xin zhangxin99@263.net

2
He Yichen 1
He Kexin 1
Xu Jiajia 3
1 https://ror.org/02drdmm93 grid.506261.6 0000 0001 0706 7839 School of Nursing, BSN, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, RN China
2 https://ror.org/02drdmm93 grid.506261.6 0000 0001 0706 7839 School of Nursing, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
3 MSN, RN, Jiaxing Municipal Heath Commission, Jiaxing, Zhejiang China
12 9 2024
12 9 2024
2024
23 6483 6 2024
2 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

A positive work environment can enhance nursing safety and patient satisfaction while alleviating nurse stress. Conversely, a poor work environment can harm nurses’ physical and mental health and compromise the quality of care, particularly in the high-intensity and shift-based setting of the ICU.

Objectives

Based on the Job demands-resources (JD-R) model, this study examined the effects of job demands and job resources in the work environment, as well as personal resources, on the night-shift alertness of ICU shift nurses.

Methods

This cross-sectional correlational exploratory study, conducted from July to September 2022, recruited 291 ICU shift nurses from a hospital in Beijing, China. The Copenhagen Psychosocial Questionnaire (COPSOQ), the Self-resilience scale, the General Self-Efficacy Scale (GSES), and the Psychomotor Vigilance Task (PVT) were used to subjectively and objectively measure the job demands, job resources, personal resources, and night-shift alertness. SPSS 26.0 and Mplus 8.3 were used to analyze the data and construct the structural equation model.

Results

The night-shift reaction time was 251.0 ms (Median), indicating a relatively high level of alertness. Job demands were negatively correlated with both job resources (r=-0.570, P < 0.001) and personal resources (r=-0.462, P < 0.001), while a positive correlation existed between job resources and personal resources (r = 0.554, P < 0.001). The results show that increased job demands can lead to higher levels of nurse strain (β = 0.955, P < 0.001), whereas job resources were found that it can decrease strain (β=-0.477, P = 0.047). Adequate job resources can enhance motivation directly (β = 0.874, P < 0.001), subsequently reducing reaction time (β=-0.148, P = 0.044) and improving night-shift alertness among ICU shift nurses.

Conclusion

Enhancing ICU shift nurses’ work motivation through bolstering job resources can boost night-shift alertness. However, it is noteworthy that, in this study, neither strain nor individual resources significantly influenced nurses’ night-shift alertness. This may be attributed to the complexity of the ICU environment and individual differences. Future research should explore the relationship between these factors and nurses’ work alertness.

Keywords

JD-R model
Night shift
Alertness
Strain
Shift work
ICU nurse
Chinese National Natural Science Foundation71603279 issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcIntroduction

The World Health Organization (WHO) emphasizes that “protecting the health and safety of healthcare workers contributes to improving their productivity, job satisfaction, and retention rates,” calling on all countries to “take necessary measures to safeguard and protect healthcare personnel at all levels“ [1]. The “Global Patient Safety Action Plan 2021–2030” also prioritizes healthcare worker safety actions as a key component of patient safety [2]. It is recognized that the hospital work environment can profoundly impact both nurses’ safety behaviors and patient safety [3]. Evidence showed that a favorable work environment enhances nursing safety and patient satisfaction while alleviating nurse stress and emotional fatigue [4–6]. Conversely, suboptimal work environments can compromise nurses’ physical and mental well-being, decreasing job satisfaction, diminishing nursing quality, and increasing turnover rates [7–9]. Given the unique demands of the ICU work environment, nurses are subjected to high-intensity workloads and extended shifts [10], resulting in adverse physical and mental health outcomes [11, 12], reduced work engagement [13, 14], and impaired clinical decision-making ability, and lead to the decline of nursing quality [15, 16]. Moreover, the rotating shift patterns in the ICU disrupt circadian rhythms, negatively impacting sleep quality and increasing nurses’ burnout during working time [17], particularly during night shifts, which escalates patient safety risks [18].

The job demands-resources (JD-R) model [19] is a theoretical framework that examines the relationship between job characteristics and occupational health, focusing on the impact of job demands and resources in the work environment on individuals [20]. According to the JD-R model, every occupation has specific factors that influence job performance, which can be categorized into two main components: job demands and resources [20]. It is believed that balancing job demands and resources is crucial for reducing work stress and fostering work motivation, thus enhancing job performance [21]. On the other hand, the JD-R model suggests that job resources can lead to higher levels of social support, which in turn positively influences the enhancement of individual resources among employees [22]; these individual resources can also empower individuals with the motivation and ability to control and influence their work environment, thereby producing better work outcomes [23, 24]. In studies focused on nurses’ job performance, two variables—self-resilience and self-efficacy—are commonly involved, as they are shown to alleviate symptoms of depression [25], reduce burnout and anxiety [26, 27], increase job engagement [28], and help achieve work goals [24]. When individuals enhance these personal resources, their well-being and job performance can improve [29]. Many studies have explored the relationship among job demands, job resources, and individual resources of nurses and their perceived workload or work stress [30, 31]; however, the impact of acute fatigue on alertness needs to be further explored.

Currently, shift work schedules in global healthcare institutions vary, primarily consisting of 8-hour- and 12-hour shifts. The 8-hour shift pattern involves frequent personnel rotations, which can somewhat alleviate nurse shortages [32]. However, this scheduling pattern can exacerbate the difficulty for nurses in adapting to shift work and increase their experience of job burnout [33, 34]. ICU nurses in many Chinese hospitals work on a 12-hour night shift. Although this scheduling pattern reduces the frequency of shifts, it also increases nurses’ workload and continuous standby time [35, 36]. The study reported an even higher (68.3%) level of burnout among 1289 ICU nurses, where working in general ICU, more years of experience, working night shifts, and personal comorbidities were associated with higher burnout [37]. Thus, it is imperative to conduct research based on the specific work environment and demands, using a combination of subjective and objective measurements to thoroughly explore the impact of job demands and resources on nurses’ alertness. The Psychomotor Vigilance Task (PVT) is a commonly used tool to assess alertness levels by measuring reaction time and sustained attention [38], serving as the most widely used objective measure of alertness [39, 40]. Previous studies have utilized this tool to measure nurses’ alertness during shifts, with longer reaction times indicating lower alertness level [41, 42] and night-shift nurses exhibiting longer reaction times than day-shift nurses [41]. Furthermore, the likelihood of medication errors during night shifts was three times higher than during day shifts (AOR = 3.1) [43]. Hence, maintaining a high level of alertness during night shifts is crucial to ensuring patient safety and the quality of nursing.

This study describes the objective status of night-shift alertness among ICU shift nurses in China. Based on the JD-R model, it explores the effect of ICU job demands, job resources and personal resources on night-shift alertness. The result will enable organizations and managers to implement targeted interventions, form supportive work environments, decrease night-shift acute fatigue among ICU shift nurses, and improve nursing performance.

Theoretical basis

Based on the JD-R model proposed by Bakker and Demerouti in 2001 (Fig. 1) [44], job demands and resources are two major categories of job characteristics. Job demands (such as quantitative demands, emotional demands, work-family conflict, etc.) refer to the objective physical, psychological, social, or organizational requirements present in the job, requiring sustained physical and/or mental effort to accomplish tasks and meet specific standards [45]. These demands are objectively present in every workplace. They are not inherently negative, but if they exceed employees’ coping ability, they can become sources of strain, leading to excessive psychological burden and eventually resulting in employee exhaustion [46], triggering the process of health impairment [44].

Job resources (including colleague support, team collaboration, development opportunities, etc.) refer to factors in the workplace that support individuals in completing tasks, providing a sense of achievement and satisfaction [44]. These resources contribute to individuals effectively coping with job demands, enhancing job satisfaction and overall well-being [45], and promoting the process of health motivation [44]. Adequate job resources can buffer the impact of job demands on stress, particularly when facing high job demands, emphasizing the crucial role of job resources in individual motivation. Like job resources, personal resources are considered positive responses to job demands in the JD-R model, contributing to maintaining individuals’ workability and motivation, ultimately influencing job performance.

Based on the above statements and previous studies, this study proposes the following hypotheses:

Hypothesis 1

There exists a correlation among the job demands, job resources, and personal resources of ICU shift nurses.

Hypothesis 2

Job demands, job resources, and personal resources can influence ICU shift nurses’ strain and/or motivation, thereby affecting their job performance (night-shift alertness).

Fig. 1 The Job demands-resources model designed by Bakker & Demerouti [44]

Methods

Study design and participants

This study was a descriptive cross-sectional survey. Using the “semTools” package in R to calculate the required sample size for the Root Mean Square Error of Approximation (RMSEA) of the structural equation model, with the model’s degrees of freedom (df) set to 10, the expected RMSEA value at 0.05, a significance level (α) of 0.05, and a power of 0.80, the required sample size was determined to be 203. The study was conducted from July to September 2022, involving 291 ICU nurses from a hospital in Beijing, China. The duration of the shift was 12 h. Each nurse was responsible for 2 to 3 patients in the ICU where the study participants worked. These patients were primarily suffering from respiratory and circulatory disorders and required life support from external devices. The inclusion criterion was Registered Nurses (RN) providing direct patient care in the ICU and participation in clinical shifts, with at least 3-night shifts during the survey month. RNs with pregnancy and a history of mental illness were excluded. Institutional review board approval was obtained for the study (No. 2022030). Informed consent was obtained from all participants before their participation.

Measurements

Sociodemographic characteristics

Sociodemographic characteristics included participants’ age, gender, educational level, marital status, number of children, and whether they had sleep problems.

Job demands

The Copenhagen Psychosocial Questionnaire (COPSOQ) II-short version [47] was utilized in this study as a 40-item Likert-type scale to evaluate the psychosocial work environment and personal health. In this study, the job demands were measured using quantitative demands, emotional demands, and work-family conflict in the COPSOQ. The intensity or frequency of each question was measured on a score range of 0 to 100. Higher scores indicated higher levels of measured dimensions. The Cronbach’s α of the COPSOQ was 0.83, and the job demands dimension was 0.84.

Job resources

The job resources of ICU shift nurses were measured using six dimensions of the COPSOQ: support from supervisors, support from colleagues, commitment to the workplace, job recognition, trust, and justice of the COPSOQ. The Likert 5-level scoring method is adopted, and the score is the average score of the item, with a total score of 100. Higher scores indicated higher levels of measured dimensions; Cronbach’s α of this dimension is 0.78.

Motivation

The motivation of ICU shift nurses was measured using two dimensions of the COPSOQ: role clarity and job satisfaction. Role Clarity was assessed using a 5-point Likert scale, while Job Satisfaction was assessed using a 4-point Likert scale, with a total score of 100 points. Higher scores indicate higher levels of the dimensions being measured; Cronbach’s α coefficient of this dimension is 0.81.

Strain

The strain of ICU shift nurses was measured using two COPSOQ dimensions: burnout and stress. And the Likert 5-level score was used, with a total score of 100. Higher scores indicated higher levels of dimensions measured. Cronbach’s α coefficient of this dimension is 0.75.

Personal resources

The Self-resilience scale, developed by Block and Kremen [48] in 1996, was utilized to assess the self-resilience level of night-shift nurses. Comprising 14 items rated on a 4-point Likert scale, with total scores ranging from 14 to 56, higher scores indicate higher levels of self-resilience. The Cronbach’s α of this scale was 0.83.

Additionally, the General Self-Efficacy Scale (GSES), developed by Schwarzer et al. [49], was employed to measure the self-efficacy of night-shift nurses. Consisting of 10 items, each rated on a 4-point Likert scale ranging from “completely incorrect” to “completely correct” (scores 1 to 4), with total scores ranging from 10 to 40; higher scores indicate higher levels of self-efficacy. The Cronbach’s α of the GSES was 0.83.

Alertness

The objective alertness level of ICU night-shift nurses is represented using reaction time. Reaction time refers to the time interval from stimulus presentation to the physical response, indicating the gap between stimulus and reaction [50]. This study measured reaction time using the Psychomotor Vigilance Task (PVT-192), widely utilized as a behavioral alertness measure [40, 51]. The PVT task lasted 5 min, recorded in milliseconds (ms), ranging from 100 to 1000 ms. Data exceeding 600 ms, anticipation, and false touches (i.e., pressing the button without the screen displaying the number) were deemed erroneous. The participants were drawn from ICU units that follow a rotating shift pattern: day shift (day 1) − 12-hour night shift (day 2) - post-night shift (day 3) - rest day (day 4) - day shift (day 5) - day shift (day 6) - rest day (day 7). Reaction time was measured during the night shift on day 2, 30 min before the start of the shift, to facilitate the implementation of measures aimed at increasing the night-shift alertness level of ICU shift nurses.

Procedures

This study used electronic surveys and on-site measurements for data collection. Before the survey, eligible shift nurses within each department were provided with standardized instructions detailing the survey’s objectives, cooperation requirements, and relevant considerations. Voluntary informed consent was obtained from participants before their enrollment. Electronic questionnaire links were then disseminated via WeChat, a popular Chinese social media platform. Participants were required to complete all questionnaire items before submission. On-site PVT measurements were conducted within the departments, with two researchers assigned to provide face-to-face guidance. They addressed any questions participants encountered during the questionnaire completion process and offered hands-on assistance with the PVT tasks. Both researchers underwent standardized training before the survey to ensure consistency and reliability.

Data analysis

We conducted data analysis using SPSS 26.0 and Mplus 8.3. Descriptive statistics were used for all variables. Frequency and percentage were employed to describe categorical data. At the same time, mean ± standard deviation (SD) was used for normally distributed continuous data, and median (upper and lower quartiles) [M(P25, P75)] was used for non-normally distributed continuous data. Single-factor analysis utilized t-tests and analysis of variance (ANOVA). Correlation analysis employed the Spearman method.

In our model, job demands, job resources, personal resources, motivation, and strain were treated as latent variables, while reaction time served as the manifest variable. Due to the skewed distribution of reaction time, the generally weighted least squares (GWLS) method was used to estimate the model accurately [52]. Model fit was assessed using chi-square (χ2), degree of freedom (df), χ2/df, Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Standardized Root Mean Square Residual (SRMR), and Root Mean Square Error of Approximation (RMSEA). Criteria for good model fit were set as CFI and TLI > 0.90, χ2/df < 3.0, SRMR < 0.05, and RMSEA < 0.08.

Results

Descriptive of the study participants and the variables

The study included 291 ICU shift nurses with an average age of 29.80 years (SD = 4.84). The majority were female (89.7%), married (56.0%), without children (56.7%), and held a bachelor’s degree or higher (73.5%). 52.6% had subjective sleep problems. The average length of work experience was 7.88 years (SD = 5.07). Results of the single-factor analysis indicated no statistically significant differences in reaction time among shift nurses of different personal characteristics (P > 0.05), as shown in Table 1.

Table 1 Characteristics and univariate analysis of the study participants (n = 291)

Variable	N(%)	RT [M(P25, P75), ms]	Z/χ2	P	
Gender			-0.107	0.915	
 Male	30(10.3)	254.00(219.38,283.50)			
 Female	261(89.7)	251.00(232.50,268.50)			
Age (years)		0.8501)	0.654	
 < 25	33(11.3)	253.00(233.75,268.00)			
 25 ~ 35	215(73.9)	250.50(230.00,268.50)			
 > 35	43(14.8)	249.50(234.00,283.00)			
Marriage			-0.414	0.679	
 Married	163(56.0)	250.00(232.50,267.50)			
 Single	128(44.0)	252.50(231.25,271.75)			
Number of children		1.0591)	0.589	
 0	165(56.7)	252.00(232.50,269.50)			
 1	105(36.1)	249.00(228.50,267.00)			
 2	21(7.2)	255.50(233.75,284.00)			
Education		-1.670	0.095	
 Diploma	77(26.5)	255.00(232.25,282.50)			
 Bachelor and above	214(73.5)	248.50(231.88,267.63)			
Work years		1.1961)	0.550	
 < 6	107(36.8)	252.50(232.50,269.00)			
 6 ~ 15	150(51.5)	250.00(228.88,268.88)			
 > 15	34(11.7)	250.50(235.38,283.50)			
Sleep problem		-0.788	0.431	
 Yes	153(52.6)	249.50(228.50,270.00)			
 No	138(47.4)	252.00(235.00,269.25)			
1): χ2

The median reaction time was 251.0 ms. Scores for job demands, job resources, and personal resources are shown in the table below. The level of quantitative demands (median = 25.00) was lower than that of emotional demands (median = 37.50) and work-family conflict (median = 33.30). Scores for job resources dimensions were generally high, with average scores of trust, justice, and support from colleagues exceeding 80. Detailed information is presented in Table 2.

Table 2 Characteristics of the measured variables in ICU shift nurse (n = 291)

Variable	Min ~ Max	Mean±SD/ Median(P25,P75)	
Job demands			
 Quantitative demands	0 ~ 75	25.00(12.50, 25.00)	
 Emotional demands	0 ~ 100	37.50(25.00, 50.00)	
 Work-family conflict	0 ~ 100	33.30(16.65, 49.95)	
Job resources			
 Support from supervisors	0 ~ 100	79.21 ± 21.51	
 Support from colleague	25 ~ 100	81.92 ± 16.67	
 Commitment to the workplace	12.50 ~ 100	73.63 ± 22.08	
 Job recognition	0 ~ 100	75.00(62.50, 87.50)	
 Trust	12.5 ~ 100	84.62 ± 16.96	
 Justice	0 ~ 100	83.20 ± 17.52	
Personal resources			
 Self-resilience	21 ~ 56	39.89 ± 7.88	
 Self-efficacy	12 ~ 40	27.43 ± 5.80	
Motivation			
 Role clarity	25 ~ 100	81.66 ± 16.55	
 Job satisfaction	33.3 ~ 100	72.23 ± 16.48	
Strain			
 Burnout	0 ~ 100	50(25, 62.5)	
 Stress	0 ~ 100	50(37.5, 62.5)	
Reaction time (ms)	193.5 ~ 515.0	251.0(232.0, 269.0)	

Correlation analysis

The correlation analysis of job demands, job resources, personal resources, motivation, strain, and reaction time of ICU shift nurses are presented in Table 3. Job demands, job resources, personal resources, motivation, and strain dimensions were all correlated. However, reaction time was only negatively correlated with support from colleagues (r=-0.132, P = 0.025) and job recognition (r=-0.119, P = 0.043).

Table 3 Correlation analysis result (n = 291, r value)

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	
Job demands																
 1. Quantitative demands	1															
 2. Emotional demands	0.311**	1														
 3. Work-family conflict	0.218**	0.503**	1													
Job resources																
 4. Support from supervisors	− 0.192**	− 0.263*	− 0.270**	1												
 5. Support from colleague	− 0.166**	− 0.203**	− 0.260**	0.604**	1											
 6. Commitment to the workplace	− 0.292**	− 0.319**	− 0.372**	0.493**	0.411**	1										
 7. Job recognition	− 0.229**	− 0.256**	− 0.218**	0.632**	0.484**	0.545**	1									
 8. Trust	− 0.233**	− 0.346**	− 0.247**	0.606**	0.402**	0.506**	0.558**	1								
 9. Justice	− 0.263**	− 0.365**	− 0.293**	0.590**	0.502**	0.520**	0.583**	0.735**	1							
Motivation																
 10. Role clarity	− 0.263**	− 0.217**	− 0.219**	0.431**	0.463**	0.509**	0.572**	0.543**	0.508**	1						
 11. Job satisfaction	− 0.258**	− 0.333**	− 0.363**	0.440**	0.423**	0.480**	0.480**	0.372**	0.477**	0.432**	1					
strain																
 12. Burnout	0.341**	0.460**	0.573**	− 0.212**	− 0.111	− 0.335**	− 0.232**	− 0.230**	− 0.277**	− 0.193**	− 0.339**	1				
 13. Stress	0.275**	0.493**	0.622**	− 0.253**	− 0.177**	− 0.328**	− 0.204**	− 0.253**	− 0.336**	− 0.159**	− 0.408**	0.798**	1			
Personal resources																
 14. Self-resilience	− 0.323**	− 0.258**	− 0.247**	0.292**	0.326**	0.277**	0.393**	0.372**	0.389**	0.348**	0.278**	− 0.318**	− 0.246**	1		
 15. Self-efficacy	− 0.328**	− 0.213**	− 0.199**	0.311**	0.312**	0.311**	0.371**	0.307**	0.315**	0.314**	0.278**	− 0.294**	− 0.255**	0.647**	1	
Reaction time	0.050	0.106	− 0.008	0.050	− 0.132*	0.068	− 0.119*	0.012	0.079	0.089	0.068	0.036	0.054	− 0.043	− 0.032	
**: P < 0.01; *: P < 0.05

The path model

Based on the JD-R model, a hypothetical model was constructed using Mplus 8.3, and the model was adjusted and fitted based on modification indices and specific circumstances, as shown in Table 4; Fig. 2.

The result indicated a negative correlation between job demands and job resources among ICU shift nurses (r=-0.570, P < 0.001), as well as a positive correlation between job resources and personal resources (r = 0.554, P < 0.001). Job demands were found to increase strain (β = 0.955, P < 0.001), while job resources can decrease ICU shift nurses’ strain (β=-0.477, P = 0.047). Furthermore, enhancing motivation (β = 0.874, P < 0.001) was observed to shorten reaction time (β=-0.148, P = 0.044) and increase night-shift alertness among ICU shift nurses. The model fitting indexes were χ2/df = 2.736, CFI = 0.923, TLI = 0.901, SRMR = 0.047, RMSEA = 0.077, indicating that this model fit well.

Table 4 Model path relationship analysis results

Model			B	SE	CR	P	β	
Job demands	<--->	Job resources	-54.494	12.083	-4.510	0.000	-0.557	
Job demands	<--->	Personal resources	-13.406	3.392	-3.952	0.000	-0.462	
Job resources	<--->	Personal resources	37.011	6.186	5.983	0.000	0.554	
Strain	<---	Job demands	2.859	0.484	5.911	0.000	0.955	
Strain	<---	Job resources	-0.230	0.116	1.990	0.047	-0.477	
Motivation	<---	Job resources	0.658	0.092	7.177	0.000	0.874	
Reaction Time	<---	Motivation	-0.471	0.233	2.019	0.044	-0.148	

Fig. 2 Structural equation model with standardized path coefficients. Job demands can increase strain, and job resources can decrease strain and enhance motivation, which can shorten reaction time. **: P < 0.01; *:P < 0.05

Discussion

This study aims to investigate the objective status of night-shift alertness among ICU shift nurses and develop a path model based on the JD-R model to explore the impact of job demands, job resources, and personal resources on night-shift alertness. The model demonstrates a negative relationship between job demands and both job resources and personal resources, while a positive relationship exists between job resources and personal resources (Hypothesis 1). Furthermore, it is concluded that job resources can reduce reaction time by enhancing motivation, thereby increasing night-shift alertness among ICU shift nurses (Hypothesis 2).

ICU shift nurses exhibited moderate night-shift alertness, with a relatively balanced alignment between job demands and resources

In this study, the nurse-patient ratio was 1:2 ~ 3. This ratio is slightly lower than the 1:2 ratio commonly found in the United States [53], but higher than the 1:1 ratio reported in Australia [54]. Under the condition of consistent staffing level, the median reaction time of ICU shift nurses before night shifts was 251.0 ms, which is consistent with the results of Ruggiero (participants were American female shift nurses) [27] but shorter than that reported by Liu (median = 288.44 ms) [41]. Liu’s study, also conducted in China, utilized PVT to assess the reaction time levels of 118 ICU nurses before and after day and night shifts and explored their influenced factors. Results indicated that factors influencing pre-night shift reaction time included gender, number of children, years of work experience, and caffeine intake. However, in our study, Table 1 demonstrates that demographic factors such as gender and number of children did not significantly impact night-shift reaction time. This discrepancy is primarily attributed to differences in work environments. Nurses’ performance is significantly influenced by the work environment [6], and in our study, the reaction time reflecting the alertness of ICU shift nurses was not influenced by individual characteristics like gender. This suggests that other factors in the work environment may play a role. Liu’s study did not explore factors of the ICU work environment, which our study aims to supplement. Furthermore, the existence of sleep problems among ICU shift nurses also had no statistical significance on the difference in night-shift alertness, which may be related to differences in the amount and/or timing of sleep before or between night shifts rather than circadian rhythm [55, 56], indicating that ICU nurses may have other adaptive mechanisms may be established to maintain the stability of night-shift alertness even if they have sleep problems.

In this study, the level of job demands and resources among ICU shift nurses was relatively balanced. Within job demands, emotional demands and work-family conflict scores were higher than quantitative demands. This suggested that the core issues within the ICU shift nurses may not lay solely within the job tasks themselves but rather focused more on emotional labor and work-family balance. This placed greater demands on nurses’ emotional investment, potentially leading to psychological burdens and fatigue [57]. The level of job resources among ICU shift nurses was generally high [42], with the colleague support score being the highest. This was consistent with the features of nursing work. Medical care tasks require close teamwork, mutual assistance, and support among colleagues, contributing to holistic patient care and enhancing nursing quality [56]. They are directly associated with reduced occupational burnout and the fostering of positive work relationships [58].

In this study, ICU shift nurses demonstrated higher scores in self-resilience and self-efficacy compared to the average scores of the scales, indicating their confidence in effectively coping with work tasks and their strong ability to recover and adapt when facing work pressure and challenges [59]. This is also related to the fact that the study subjects are ICU nurses who often need strong psychological adjustment and coping skills to handle high-intensity work environments and unexpected situations [60, 61].

The relationship between job demands, job resources, and personal resources is consistent with the JD-R model

The results (Table 4; Fig. 2) demonstrated a significant negative correlation between job demands and both job resources and personal resources, consistent with the JD-R model [46]. On the one hand, the limited availability of both job and personal resources can intensify the perceived pressure of job demands among nurses; on the other hand, it may lead to competition and conflict among nurses for resources, exacerbating the bad effects of job demands [31]. This calls for managerial intervention to improve the work environment, optimize job resources, and simultaneously enhance nurses’ personal resources, fostering a balanced work ecosystem to alleviate the adverse effects of job demands among ICU shift nurses.

In the framework of the JD-R model, there are multifaceted positive relationships between job and personal resources [62]. Firstly, by providing support and protective mechanisms, job resources can mitigate the negative impact of job demands faced by individuals [63]. Secondly, job resources can foster individual professional development and growth by offering training and developmental opportunities; they enhance individuals’ professional competence, thereby bolstering their confidence and capability to tackle various work challenges [24]. Moreover, job resources play a crucial role in establishing social support networks. Positive colleague relationships and support contribute to heightened perceived levels of social support, facilitating individuals’ resilience in coping with stress and adversity [22].

Furthermore, social support serves as a significant source of self-efficacy. Encouragement and support from colleagues instill greater belief in individuals’ ability to accomplish tasks [22, 64]. Additionally, a strong sense of job cognition and a fair, trusted work environment can cultivate a positive psychological atmosphere, enhancing individuals’ emotional and mental well-being and reducing emotional fatigue [65, 66]. These factors collectively create favorable conditions for the utilization of personal resources.

Effects on night-shift alertness

The model (Fig. 2) illustrated that adequate job resources positively impacted motivation, while a high level of motivation can increase the night-shift alertness of ICU shift nurses. As described above, sufficient job resources can support and assist nurses to engage more actively in their work. When nurses feel more motivated at work, they are inclined to focus on tasks, value work quality more, and thus maintain high alertness and attention [45]. Nursing managers should ensure ample job resources, emphasize the motivation level of ICU shift nurses, and foster a supportive team atmosphere. Regular evaluation of nurses’ job performance should also be conducted to identify and address issues promptly.

The model (Fig. 2) also indicated that the job demands of ICU shift nurses can increase their strain, while job resources can reduce strain. A complex and high-intensity work environment with elevated job demands can induce pressure on nurses. Yet, in the presence of adequate support, training, and resources, ICU shift nurses are better equipped to manage work pressure [31, 67]. Notably, we did not observe a significant impact of strain on night-shift reaction time. Possible reasons for this could be twofold: (1) Sample characteristics: Model outcomes are influenced by the characteristics of the study sample [68]. Due to their cultivated professional skills and experience in high-pressure work environments, ICU shift nurses may manage work stress effectively through adaptive strategies or self-regulation mechanisms [69]. Thus, even when experiencing some pressure, it may not significantly affect their alertness. (2) “Supernurse” Culture: Henshall et al. [70] found a “supernurse” phenomenon in the professional culture of nursing. This includes enduring hardship, being willing to sacrifice, having a strong sense of responsibility, and believing in self-sacrifice for the greater good. This culture compels nurses to overcome strain and maintain alertness in their work, even when facing significant stress. (3) Interventions by other factors: In this model, the strain may have been influenced by other unaccounted factors, resulting in its lack of impact on night-shift reaction time. Future research should explore the relationship between individual psychological characteristics and job performance in high-pressure work environments.

Our study did not observe a significant impact of personal resources on strain, motivation, and night-shift alertness among ICU shift nurses. This suggested that the role of personal resources in the ICU nurses may not be as significant as expected. The ICU work environment is exceptionally complex, involving high-intensity nursing tasks and unpredictable medical situations [10]. In such work environments, the effect of personal resources may be interfered with or weakened by other factors, such as the suddenness and urgency of job demands. Additionally, teamwork and organizational support are more critical in the ICU environment than personal resources [71]. Nurses rely more on teamwork and leadership support to cope with work pressure, while the role of personal resources is relatively minor. However, this may also be related to our study’s dimension selection limitations and sample size. Nonetheless, this does not imply that nurses’ personal resources are entirely ineffective. Previous studies have shown that high levels of personal resources lead nurses to take a more positive view of the work environment, proactively optimize job demands, adjust their perception levels of job resources [72, 73], alleviate fatigue, stress, and other adverse effects of work, and are associated with higher job satisfaction, work performance, and lower absenteeism rates [74].

Applicability of the JD-R model in ICU shift nurses

Based on the above results, it appeared that there are some discrepancies with the traditional JD-R model assumptions, which may impact its applicability within this specific cohort: (1) Insignificant influence of personal resources: In the JD-R model, personal resources are typically presumed to affect strain and motivation positively. However, within the ICU shift nurses, personal resources were not observed to affect strain, motivation, or night-shift alertness significantly. This suggests that the role of personal resources may not be as pronounced in this cohort as anticipated, or other factors may be at play. (2) Influence of other factors: Besides the factors considered in the JD-R model, such as job demands, job resources, personal resources, motivation, and strain, there may be additional factors influencing the job performance of ICU shift nurses. Factors such as specific work environment, teamwork atmosphere, and complexity of job tasks might have a greater impact on their job performance. (3) Necessity for model revision: Based on our findings, Hypotheses 1 and 2 are supported in the context of ICU shift nurses. There is a significant correlation among job demands, job resources, and personal resources. Additionally, these factors influence strain and motivation, subsequently affecting night-shift alertness. However, our study’s model diverges from the JD-R model. To enhance the model’s explanatory power and applicability, we recommend appropriate adjustments to the JD-R model to capture better ICU shift nurses’ unique work characteristics and influencing factors.

In summary, while the JD-R model provides a useful framework for understanding the relationship between work strain, motivation, and job performance, its applicability may be subject to certain limitations within specific groups. When applying the JD-R model to ICU shift nurses, careful consideration should be given to the validity of the model’s assumptions, and modifications and adjustments should be made based on the specific circumstances to accommodate this group’s characteristics better.

Limitations

While our research has made strides in uncovering key factors in ICU nursing work, there are still limitations to consider. For instance, our study focused solely on ICU shift nurses at a single location. In the selection and competence of ICU nurses, there is a certain degree of selection and survivorship bias. ICU nurses typically need to possess specific skills and qualities. This means that our study sample may not fully represent the entire nursing population, as the results are skewed toward those nurses who can endure the long-term pressures of ICU work. To mitigate these effects, we recommend that future research employ broader recruitment strategies, including different types of nursing personnel. This would enhance the diversity and representativeness of the sample, providing a more comprehensive perspective.

Furthermore, the job demands, resources, motivations, strains, and personal resources in nursing are multifaceted. Our study only classified them based on the JD-R model and the COPSOQ, which is not exhaustive. Future research could consider incorporating additional physiological and psychological measurement tools to assess nursing work factors from multiple perspectives, thereby providing a more comprehensive evaluation of the impact of the nursing work environment on nursing professionals. Additionally, future research and practice should focus on the effects of cultural factors on nurse performance and implement measures to change the work environment and culture. This would help alleviate nurse burnout and enhance job satisfaction and efficiency.

Conclusion

This study demonstrated that the JD-R model exhibits a certain degree of adaptability among ICU shift nurses. High job demands positively influence strain, while job resources buffer against strain, alleviating nurse strain with provided support. Job resources positively impact motivation, increasing alertness during night shifts. However, personal resources do not significantly influence strain, motivation, or night-shift alertness, nor does strain. This may be attributed to the specific and complex nature of the ICU work environment and individual differences among nurses, warranting further investigation. Job resources significantly reduce strain, enhance motivation, and improve night-shift alertness, highlighting their importance. Managers should reinforce job resources within the ICU environment to cultivate a supportive work environment and increase nurses’ job alertness.

Acknowledgements

Acknowledgment and sincere thanks to the nurses who participated in this study.

Author contributions

Conceptualization, methodology, and formal analysis: G.-J.Y., Z.-X.; investigation: G.-J.Y., H.-Y.C., H.-K.X, X.-J.J.; writing—original draft: G.-J.Y., H.-Y.C., H.-K.X.; writing—review and editing: Z.-X., G.-J.Y.; data curation: Z.-X. All authors have read and agreed to the published version of the manuscript.

Funding

Chinese National Natural Science Foundation [71603279] has supported this project.

Data availability

The datasets generated and/or analysed during the present study are not publicly available due to the data being proprietary and confidential records of Chinese Academy of Medical Sciences & Peking Union Medical College, but are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was approved by the Institutional Review Board of Peking Union Medical College (No. 2022030). It was conducted in accordance with the principles outlined in the Declaration of Helsinki. All participants were informed about the study details before participation, including its purpose, procedures, potential risks, benefits, confidentiality measures, and voluntary involvement. Informed consent was obtained from all participants, and the survey was conducted following relevant procedural guidelines.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. WHO. Occupational health: health workers. https://www.who.int/news-room/fact-sheets/detail/occupational-health--health-workers.Accessed
2. Global patient safety action. plan 2021–2030: towards eliminating avoidable harm in health care. In. Geneva: World Health Organization; 2021.
3. de Bienassis K, Slawomirski L, Klazinga NS. The economics of patient safety Part IV: Safety in the workplace: Occupational safety as the bedrock of resilient health systems. In.: OECD Publishing; 2021.
4. Copanitsanou P Fotos N Brokalaki H Effects of work environment on patient and nurse outcomes Br J Nurs 2017 26 3 172 6 10.12968/bjon.2017.26.3.172 28185485
Copanitsanou P, Fotos N, Brokalaki H. Effects of work environment on patient and nurse outcomes. Br J Nurs. 2017;26(3):172–6. 10.12968/bjon.2017.26.3.172.28185485 10.12968/bjon.2017.26.3.172
5. Nantsupawat A, Kunaviktikul W, Nantsupawat R, Wichaikhum OA, Thienthong H, Poghosyan L. Effects of nurse work environment on job dissatisfaction, burnout, intention to leave. Int Nurs Rev 2017,64(1):91–8.10.1111/inr.12342
6. Pogue CA Li P Swiger P Gillespie G Ivankova N Patrician PA Associations among the nursing work environment, nurse-reported workplace bullying, and patient outcomes Nurs Forum 2022 57 6 1059 68 10.1111/nuf.12781 35908259
Pogue CA, Li P, Swiger P, Gillespie G, Ivankova N, Patrician PA. Associations among the nursing work environment, nurse-reported workplace bullying, and patient outcomes. Nurs Forum. 2022;57(6):1059–68. 10.1111/nuf.12781.35908259 10.1111/nuf.12781
7. Ilić IM, Arandjelović M, Jovanović JM, Nešić MM. Relationships of work-related psychosocial risks, stress, individual factors and burnout - questionnaire survey among emergency physicians and nurses. Med Pr 2017,68(2):167–78.10.13075/mp.5893.00516
8. Nützi M Koch P Baur H Elfering A Work-Family, Conflict, Task interruptions, and influence at Work Predict Musculoskeletal Pain in operating room nurses Saf Health Work 2015 6 4 329 37 10.1016/j.shaw.2015.07.011 26929846
Nützi M, Koch P, Baur H, Elfering A. Work-Family, Conflict, Task interruptions, and influence at Work Predict Musculoskeletal Pain in operating room nurses. Saf Health Work. 2015;6(4):329–37. 10.1016/j.shaw.2015.07.011.26929846 10.1016/j.shaw.2015.07.011
9. Sundstrup E Andersen LL Joint association of physical and psychosocial working conditions with risk of long-term sickness absence: prospective cohort study with register follow-up Scand J Public Health 2021 49 2 132 40 10.1177/1403494820936423 32597327
Sundstrup E, Andersen LL. Joint association of physical and psychosocial working conditions with risk of long-term sickness absence: prospective cohort study with register follow-up. Scand J Public Health. 2021;49(2):132–40. 10.1177/1403494820936423.32597327 10.1177/1403494820936423
10. Thompson BJ Fatigue and the female nurse: a narrative review of the current state of research and future directions Womens Health Rep (New Rochelle) 2021 2 1 53 61 10.1089/whr.2020.0107 33786531
Thompson BJ. Fatigue and the female nurse: a narrative review of the current state of research and future directions. Womens Health Rep (New Rochelle). 2021;2(1):53–61. 10.1089/whr.2020.0107.33786531 10.1089/whr.2020.0107
11. Lönnqvist K Sinervo T Kaihlanen AM Vehviläinen-Julkunen K Elovainio M Psychosocial work characteristics and sleep quality among early career registered nurses: a cross-sectional latent profile analysis BMC Health Serv Res 2023 23 1 1020 10.1186/s12913-023-09949-9 37735692
Lönnqvist K, Sinervo T, Kaihlanen AM, Vehviläinen-Julkunen K, Elovainio M. Psychosocial work characteristics and sleep quality among early career registered nurses: a cross-sectional latent profile analysis. BMC Health Serv Res. 2023;23(1):1020. 10.1186/s12913-023-09949-9.37735692 10.1186/s12913-023-09949-9
12. Zhou J Zhang KF How increased job demand affects nurses’ task mastery and deviance in the pandemic era J Adv Nurs 2022 78 8 2349 56 10.1111/jan.15145 34978106
Zhou J, Zhang KF. How increased job demand affects nurses’ task mastery and deviance in the pandemic era. J Adv Nurs. 2022;78(8):2349–56. 10.1111/jan.15145.34978106 10.1111/jan.15145
13. Mazzetti G Robledo E Vignoli M Topa G Guglielmi D Schaufeli WB Work Engagement: a meta-analysis using the job demands-resources Model Psychol Rep 2023 126 3 1069 107 10.1177/00332941211051988 34886729
Mazzetti G, Robledo E, Vignoli M, Topa G, Guglielmi D, Schaufeli WB. Work Engagement: a meta-analysis using the job demands-resources Model. Psychol Rep. 2023;126(3):1069–107. 10.1177/00332941211051988.34886729 10.1177/00332941211051988
14. Kato Y, Chiba R, Shimazu AW. Engagement and the validity of job demands-resources Model among nurses in Japan: A literature review. Workplace Health Saf 2021,69(7):323–42.10.1177/21650799211002471
15. Gómez-Salgado J Domínguez-Salas S Romero-Martín M Romero A Coronado-Vázquez V Ruiz-Frutos C Work engagement and psychological distress of health professionals during the COVID-19 pandemic J Nurs Manag 2021 29 5 1016 25 10.1111/jonm.13239 33400325
Gómez-Salgado J, Domínguez-Salas S, Romero-Martín M, Romero A, Coronado-Vázquez V, Ruiz-Frutos C. Work engagement and psychological distress of health professionals during the COVID-19 pandemic. J Nurs Manag. 2021;29(5):1016–25. 10.1111/jonm.13239.33400325 10.1111/jonm.13239
16. Labrague LJ de Los Santos JAA Fear of COVID-19, psychological distress, work satisfaction and turnover intention among frontline nurses J Nurs Manag 2021 29 3 395 403 10.1111/jonm.13168 32985046
Labrague LJ, de Los Santos JAA. Fear of COVID-19, psychological distress, work satisfaction and turnover intention among frontline nurses. J Nurs Manag. 2021;29(3):395–403. 10.1111/jonm.13168.32985046 10.1111/jonm.13168
17. Sundin L Hochwälder J Lisspers J A longitudinal examination of generic and occupational specific job demands, and work-related social support associated with burnout among nurses in Sweden Work 2011 38 4 389 400 10.3233/WOR-2011-1142 21508528
Sundin L, Hochwälder J, Lisspers J. A longitudinal examination of generic and occupational specific job demands, and work-related social support associated with burnout among nurses in Sweden. Work. 2011;38(4):389–400. 10.3233/WOR-2011-1142.21508528 10.3233/WOR-2011-1142
18. Härmä M Karhula K Puttonen S Ropponen A Koskinen A Ojajärvi A Kivimäki M Shift work with and without night work as a risk factor for fatigue and changes in sleep length: a cohort study with linkage to records on daily working hours J Sleep Res 2019 28 3 e12658 10.1111/jsr.12658 29383788
Härmä M, Karhula K, Puttonen S, Ropponen A, Koskinen A, Ojajärvi A, Kivimäki M. Shift work with and without night work as a risk factor for fatigue and changes in sleep length: a cohort study with linkage to records on daily working hours. J Sleep Res. 2019;28(3):e12658. 10.1111/jsr.12658.29383788 10.1111/jsr.12658
19. Demerouti E Bakker AB Nachreiner F Schaufeli WB The job demands-resources model of burnout J Appl Psychol 2001 86 3 499 512 10.1037/0021-9010.86.3.499 11419809
Demerouti E, Bakker AB, Nachreiner F, Schaufeli WB. The job demands-resources model of burnout. J Appl Psychol. 2001;86(3):499–512.11419809 10.1037/0021-9010.86.3.499
20. Bakker AB, Demerouti E. The job demands-resources model: state of the art. J Managerial Psychol 2007,22(3):309–28.10.1108/02683940710733115
21. Bakker AB Demerouti E Euwema MC Job resources buffer the impact of job demands on burnout J Occup Health Psychol 2005 10 2 170 80 10.1037/1076-8998.10.2.170 15826226
Bakker AB, Demerouti E, Euwema MC. Job resources buffer the impact of job demands on burnout. J Occup Health Psychol. 2005;10(2):170–80. 10.1037/1076-8998.10.2.170.15826226 10.1037/1076-8998.10.2.170
22. Kerksieck P, Bauer GF, Brauchli R. Personal and Social resources at Work: reciprocal relations between crafting for Social Job resources, Social Support at Work and Psychological Capital. Front Psychol 2019,10:263210.3389/fpsyg.2019.02632
23. Bakker AB. Demerouti E.Job demands–resources theory. Wellbeing: A complete reference guide.2014:1–28.
24. Bakker AB, de Vries JD. Job demands-resources theory and self-regulation: new explanations and remedies for job burnout. Anxiety Stress Coping 2021,34(1):1–21.10.1080/10615806.2020.1797695
25. Sousa LRM, Leoni PHT, Carvalho RAG, Ventura CAA, Silva A, Reis RK, Gir E. Resilience, depression and self-efficacy among Brazilian nursing professionals during the COVID-19 pandemic. Cien Saude Colet 2023,28(10):2941–50.10.1590/1413-812320232810.09852023
26. Georges MT, Roberts LR, Johnston Taylor E, Nick JM, Dehom S. Burnout, Self-Efficacy, and Resilience in Haitian nurses: a cross-sectional study. J Holist Nurs 2022,40(4):310–25.10.1177/08980101211065600
27. Ruggiero JS Redeker NS Fiedler N Avi-Itzhak T Fischetti N Sleep and psychomotor vigilance in female shiftworkers Biol Res Nurs 2012 14 3 225 35 10.1177/1099800411408413 21708893
Ruggiero JS, Redeker NS, Fiedler N, Avi-Itzhak T, Fischetti N. Sleep and psychomotor vigilance in female shiftworkers. Biol Res Nurs. 2012;14(3):225–35. 10.1177/1099800411408413.21708893 10.1177/1099800411408413
28. Cabrera-Aguilar E, Zevallos-Francia M, Morales-García M, Ramírez-Coronel AA, Morales-García SB, Sairitupa-Sanchez LZ, Morales-García WC. Resilience and stress as predictors of work engagement: the mediating role of self-efficacy in nurses. Front Psychiatry. 2023;14(1202048). 10.3389/fpsyt.2023.1202048.
29. Lupșa D Vîrga D Maricuțoiu LP Rusu A Increasing Psychological Capital: a pre-registered Meta-analysis of controlled interventions Appl Psychol 2020 69 4 1506 56 10.1111/apps.12219
Lupșa D, Vîrga D, Maricuțoiu LP, Rusu A. Increasing Psychological Capital: a pre-registered Meta-analysis of controlled interventions. Appl Psychol. 2020;69(4):1506–56. 10.1111/apps.12219.10.1111/apps.12219
30. Kwak SK, Ahn JS, Kim YH. The Association of Job Stress, Quality of Sleep, and the Experience of Near-Miss Errors among Nurses in General Hospitals. Healthcare (Basel).2024,12(6).10.3390/healthcare12060699
31. Kaihlanen AM Ruotsalainen S Väisänen V Corneliusson L Pesonen T Sinervo T Job demand and job resource factors explaining stress and job satisfaction among home care nurses - a mixed-methods sequential explanatory study BMC Nurs 2023 22 1 404 10.1186/s12912-023-01568-3 37891583
Kaihlanen AM, Ruotsalainen S, Väisänen V, Corneliusson L, Pesonen T, Sinervo T. Job demand and job resource factors explaining stress and job satisfaction among home care nurses - a mixed-methods sequential explanatory study. BMC Nurs. 2023;22(1):404. 10.1186/s12912-023-01568-3.37891583 10.1186/s12912-023-01568-3
32. Xin S, Jiang W, Xin Z. Changes in Chinese nurses’ mental health during 1998–2016: A cross-temporal meta-analysis. Stress Health 2019,35(5):665–74.10.1002/smi.2907
33. Chen S Wu H Sun M Wei Q Zhang Q Effects of shift work schedules, compensatory sleep, and work-family conflict on fatigue of shift-working nurses in Chinese intensive care units Nurs Crit Care 2023 28 6 948 56 10.1111/nicc.12909 37078518
Chen S, Wu H, Sun M, Wei Q, Zhang Q. Effects of shift work schedules, compensatory sleep, and work-family conflict on fatigue of shift-working nurses in Chinese intensive care units. Nurs Crit Care. 2023;28(6):948–56. 10.1111/nicc.12909.37078518 10.1111/nicc.12909
34. Khan WAA, Conduit R, Kennedy GA, Jackson ML. The relationship between shift-work, sleep, and mental health among paramedics in Australia. Sleep Health 2020,6(3):330–7.10.1016/j.sleh.2019.12.002
35. Matheson A, O’Brien L, Reid JA. The impact of shiftwork on health: a literature review. J Clin Nurs 2014,23(23–24):3309–20.10.1111/jocn.12524
36. Gifkins J Johnston A Loudoun R Troth A Fatigue and recovery in shiftworking nurses: a scoping literature review Int J Nurs Stud 2020 112 103710 10.1016/j.ijnurstu.2020.103710 32912638
Gifkins J, Johnston A, Loudoun R, Troth A. Fatigue and recovery in shiftworking nurses: a scoping literature review. Int J Nurs Stud. 2020;112:103710. 10.1016/j.ijnurstu.2020.103710.32912638 10.1016/j.ijnurstu.2020.103710
37. Hu Z Wang H Xie J Zhang J Li H Liu S Li Q Yang Y Huang Y Burnout in ICU doctors and nurses in mainland China-A national cross-sectional study J Crit Care 2021 62 265 70 10.1016/j.jcrc.2020.12.029 33477093
Hu Z, Wang H, Xie J, Zhang J, Li H, Liu S, Li Q, Yang Y, Huang Y. Burnout in ICU doctors and nurses in mainland China-A national cross-sectional study. J Crit Care. 2021;62:265–70. 10.1016/j.jcrc.2020.12.029.33477093 10.1016/j.jcrc.2020.12.029
38. Zhang X, Dai X, Jiao J, Lee S-Y. .Impact of sleep-wake features on fatigue among female shift work nurses. Ann Med 2023,55(1):2210843.10.1080/07853890.2023.2210843
39. Dinges DF, Powell JW. Microcomputer analyses of performance on a portable, simple visual RT task during sustained operations. Behav Res Methods Instruments Computers 1985,17(6):652–5.10.3758/BF03200977
40. Doran SM Van Dongen HP Dinges DF Sustained attention performance during sleep deprivation: evidence of state instability Arch Ital Biol 2001 139 3 253 67 11330205
Doran SM, Van Dongen HP, Dinges DF. Sustained attention performance during sleep deprivation: evidence of state instability. Arch Ital Biol. 2001;139(3):253–67.11330205
41. Liu Y, Ji J, Sun J, Zhang X. Characteristics and influencing factors of the reaction time among nurses in the lntensive care unit. Chin Nurs Management(China) 2020,20:413–7.
42. Xu J. The Mechanism of ICU Nurse Job Crafting on work Fatigue Based on the Job Demands-Resources Model (Chinese thesis). Chinese Academy of Medical Sciences & Peking Union Medical College; 2023.
43. Feleke SA, Mulatu MA, Yesmaw YS. Medication administration error: magnitude and associated factors among nurses in Ethiopia. BMC Nurs 2015 14:5310.1186/s12912-015-0099-1
44. Bakker AB, Demerouti E. Job demands-resources theory: taking stock and looking forward. J Occup Health Psychol 2017,22(3):273–85.10.1037/ocp0000056
45. Dan X, Huang Y, Ren JH, Tian YL, He YL. Professional nursing practice Environment and Work Engagement: the mediating roles of self-efficacy and achievement motivation. J Nurs Res 2023,31(4):e285.10.1097/jnr.0000000000000563
46. Bakker AB, Van Emmerik H, Van Riet. P.How job demands, resources, and burnout predict objective performance: a constructive replication. Anxiety Stress Coping 2008,21(3):309–24.10.1080/10615800801958637
47. Li J, Fu H, Hu Y, Shang L, Wu Y, Kristensen TS, Mueller BH, Hasselhorn HM. Psychosocial work environment and intention to leave the nursing profession: results from the longitudinal Chinese NEXT study. Scand J Public Health 2010,38(3 Suppl):69–80.10.1177/1403494809354361
48. Block J Kremen AM IQ and ego-resiliency: conceptual and empirical connections and separateness J Pers Soc Psychol 1996 70 2 349 61 10.1037/0022-3514.70.2.349 8636887
Block J, Kremen AM. IQ and ego-resiliency: conceptual and empirical connections and separateness. J Pers Soc Psychol. 1996;70(2):349–61. 10.1037/0022-3514.70.2.349.8636887 10.1037/0022-3514.70.2.349
49. Schwarzer R Bäßler J Kwiatek P Schröder K Zhang JX The Assessment of optimistic Self-beliefs: comparison of the German, Spanish, and Chinese versions of the general self-efficacy scale Appl Psychol 1997 46 1 69 88 10.1111/j.1464-0597.1997.tb01096.x
Schwarzer R, Bäßler J, Kwiatek P, Schröder K, Zhang JX. The Assessment of optimistic Self-beliefs: comparison of the German, Spanish, and Chinese versions of the general self-efficacy scale. Appl Psychol. 1997;46(1):69–88. 10.1111/j.1464-0597.1997.tb01096.x.10.1111/j.1464-0597.1997.tb01096.x
50. Yu X, Lu S, Wu J, Miyamoto S. Research on reaction time and the kinetic perimetry. Comput Eng Design(China) 2013,34(10):3606–12.10.16208/j.issn1000-7024.2013.10.067
51. Dinges DF Pack F Williams K Gillen KA Powell JW Ott GE Aptowicz C Pack AI Cumulative sleepiness, mood disturbance, and psychomotor vigilance performance decrements during a week of sleep restricted to 4–5 hours per night Sleep 1997 20 4 267 77 9231952
Dinges DF, Pack F, Williams K, Gillen KA, Powell JW, Ott GE, Aptowicz C, Pack AI. Cumulative sleepiness, mood disturbance, and psychomotor vigilance performance decrements during a week of sleep restricted to 4–5 hours per night. Sleep. 1997;20(4):267–77.9231952
52. Diamantopoulos A, Siguaw JA, Siguaw JA. Introducing LISREL: a guide for the uninitiated. Sage; 2000.
53. Coffman JM Seago JA Spetz J Minimum nurse-to-patient ratios in acute care hospitals in California Health Aff (Millwood) 2002 21 5 53 64 10.1377/hlthaff.21.5.53 12224909
Coffman JM, Seago JA, Spetz J. Minimum nurse-to-patient ratios in acute care hospitals in California. Health Aff (Millwood). 2002;21(5):53–64. 10.1377/hlthaff.21.5.53.12224909 10.1377/hlthaff.21.5.53
54. Law AC, Stevens JP, Hohmann S, Walkey AJ. Patient outcomes after the introduction of Statewide ICU nurse staffing regulations. Crit Care Med 2018,46(10):1563–9.10.1097/ccm.0000000000003286
55. Reiter AM, Sargent C, Roach GD. No effect of Chronotype on Sleepiness, Alertness, and sustained attention during a single night shift. Clocks Sleep 2021,3(3):377–86.10.3390/clockssleep3030024
56. Juda M, Vetter C, Roenneberg T. Chronotype modulates sleep duration, sleep quality, and social jet lag in shift-workers. J Biol Rhythms 2013,28(2):141–51.10.1177/0748730412475042
57. Ferramosca FMP, De Maria M, Ivziku D, Raffaele B, Lommi M, Tolentino Diaz MY, Montini G, Porcelli B, De Benedictis A, Tartaglini D et al. Nurses’ Organization of Work and Its Relation to Workload in Medical Surgical Units: A Cross-Sectional Observational Multi-Center Study. Healthcare (Basel).2023,11(2).10.3390/healthcare11020156
58. Kilroy S Bosak J Chênevert D Flood PC Hill K Reducing burnout among nurses: the role of high-involvement work practices and colleague support Health Care Manage Rev 2022 47 2 115 24 10.1097/hmr.0000000000000304 33428348
Kilroy S, Bosak J, Chênevert D, Flood PC, Hill K. Reducing burnout among nurses: the role of high-involvement work practices and colleague support. Health Care Manage Rev. 2022;47(2):115–24. 10.1097/hmr.0000000000000304.33428348 10.1097/hmr.0000000000000304
59. Cichoń J Płaszewska-Żywko L Kózka M Emotional intelligence and coping strategies among intensive care unit nurses Nurs Crit Care 2023 28 2 322 8 10.1111/nicc.12839 36054698
Cichoń J, Płaszewska-Żywko L, Kózka M. Emotional intelligence and coping strategies among intensive care unit nurses. Nurs Crit Care. 2023;28(2):322–8. 10.1111/nicc.12839.36054698 10.1111/nicc.12839
60. Wu J, Li Y, Lin Q, Zhang J, Liu Z, Liu X, Rong X, Zhong X. The effect of occupational coping self-efficacy on presenteeism among ICU nurses in Chinese public hospitals: a cross-sectional study. Front Psychol 2024 15:134724910.3389/fpsyg.2024.1347249
61. Li H, Xu YL, Jing MJ, Wei XJ, Li LM, Guo YF. The mediating effects of adversity quotient and self-efficacy on ICU nurses’ organizational climate and work engagement. J Nurs Manag 2022,30(7):3322–9.10.1111/jonm.13773
62. Xanthopoulou D Bakker AB Demerouti E Schaufeli WB Reciprocal relationships between job resources, personal resources, and work engagement J Vocat Behav 2009 74 3 235 44 10.1016/j.jvb.2008.11.003
Xanthopoulou D, Bakker AB, Demerouti E, Schaufeli WB. Reciprocal relationships between job resources, personal resources, and work engagement. J Vocat Behav. 2009;74(3):235–44.10.1016/j.jvb.2008.11.003
63. Bakker AB Demerouti E Sanz-Vergel AI Burnout and work engagement: the JD–R approach Annu Rev Organ Psychol Organ Behav 2014 1 1 389 411 10.1146/annurev-orgpsych-031413-091235
Bakker AB, Demerouti E, Sanz-Vergel AI. Burnout and work engagement: the JD–R approach. Annu Rev Organ Psychol Organ Behav. 2014;1(1):389–411.10.1146/annurev-orgpsych-031413-091235
64. Benight CC, Bandura A. Social cognitive theory of posttraumatic recovery: the role of perceived self-efficacy. Behav Res Ther 2004,42(10):1129–48.10.1016/j.brat.2003.08.008
65. Väänänen A, Koskinen A, Joensuu M, Kivimäki M, Vahtera J, Kouvonen A. Jäppinen P.Lack of predictability at work and risk of acute myocardial infarction: an 18-year prospective study of industrial employees. Am J Public Health 2008,98(12):2264–71.10.2105/ajph.2007.122382
66. Petitta L, Jiang L, Härtel CEJ. Emotional contagion and burnout among nurses and doctors: do joy and anger from different sources of stakeholders matter? Stress Health 2017,33(4):358–69.10.1002/smi.2724
67. Lu J, Yu Y, Zhao Y, Jenkin M. The correlation between workers’ working pressure and physical and mental health analyzed by the job demand-resource stress model. Work 2021,69(2):573–83.10.3233/wor-213500
68. Ottenstein C, Werner L. Compliance in ambulatory Assessment studies: investigating study and sample characteristics as predictors. Assess 2022,29(8):1765–76.10.1177/10731911211032718
69. Wei W, Niu Y, Ge X. Core competencies for nurses in Chinese intensive care units: a cross-sectional study. Nurs Crit Care 2019,24(5):276–82.10.1111/nicc.12398
70. Steege LM, Rainbow JG. Fatigue in hospital nurses - ‘Supernurse’ culture is a barrier to addressing problems: A qualitative interview study. Int J Nurs Stud 2017,67:20–810.1016/j.ijnurstu.2016.11.014
71. Cornell P, Townsend Gervis M, Yates L, Vardaman JM. Impact of SBAR on Nurse Shift reports and Staff rounding. Medsurg nursing.2014,23(5).
72. Tims M Bakker AB Derks D Development and validation of the job crafting scale J Vocat Behav 2012 80 1 173 86 10.1016/j.jvb.2011.05.009
Tims M, Bakker AB, Derks D. Development and validation of the job crafting scale. J Vocat Behav. 2012;80(1):173–86.10.1016/j.jvb.2011.05.009
73. Demerouti E Peeters MC Transmission of reduction-oriented crafting among colleagues: a diary study on the moderating role of working conditions J Occup Organizational Psychol 2018 91 2 209 34 10.1111/joop.12196
Demerouti E, Peeters MC. Transmission of reduction-oriented crafting among colleagues: a diary study on the moderating role of working conditions. J Occup Organizational Psychol. 2018;91(2):209–34.10.1111/joop.12196
74. Brown R Wey H Foland K The relationship among change fatigue, resilience, and job satisfaction of hospital staff nurses J Nurs Scholarsh 2018 50 3 306 13 10.1111/jnu.12373 29517141
Brown R, Wey H, Foland K. The relationship among change fatigue, resilience, and job satisfaction of hospital staff nurses. J Nurs Scholarsh. 2018;50(3):306–13.29517141 10.1111/jnu.12373
