The prevalence and factors of work-related fatigue among nurses: a meta-analysis and systematic review

The prevalence and factors of work-related fatigue among nurses: a meta-analysis and systematic review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The prevalence and factors of work-related fatigue among nurses: a meta-analysis and systematic review Rong Pi, Yunfang Liu, Zong De, Yali Wan, Yi Chen, Zihan He, Wenjing Li, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4334355/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Numerous studies have evaluated the level of work-related fatigue in nurses and its risk factors. However, none of these studies have rigorously assessed these through meta-analysis and systematic review. Objectives This study aimed to examine work-related fatigue among nurses and identify the factors that influence it. Method The review searched eight databases, including PubMed, Web of Science, Scopus, CINAHL, PsycINFO, and Chinese databases such as China National Knowledge Infrastructure (CNKI), Chinese Biological Medical (CBM), and WanFang Database. The temporal scope of the search encompasses a temporal interval commencing from inception and extending to October 2023. The PRISMA guideline was used to report the meta-analysis and systematic review. The research team conducted a comprehensive study of selection, quality assessments, data extraction and analysis of all included literature. The means and standard deviations of three subscales of work-related fatigue were pooled using random effects meta-analysis using Stata 18.0 software. The registration PROSPERO number is CRD42023456337. Results A total of 3259 initial studies were retrieved, of which 48 qualified articles were finally included in this article. The pooled mean scores of acute fatigue(AF), chronic fatigue(CF) and inter-shift recovery(IR) was 62.99(95% CI: 59.38–66.4), 51.30(95% CI: 46.82–55.78) and 49.15(95% CI: 43.92–54.37). Furthermore, the factors influencing nurses’ work-related fatigue included both organizational and individual variables. Conclusion Clinical nurses were at a moderately high level of acute and chronic fatigue. Four themes were summarized as factors influencing the level of work-related fatigue among nurses, including organizational culture, Management system, individual capability and individual acts. Consequently, Nurse’ work-related fatigue is a vital global issue that roots in multiple causes, and more resources should be used to improve current situation. Work-related fatigue Risky factors Prevalence Clinical nurses Meta-analysis Systematic review Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Nurses are the largest group of healthcare professionals and a vital part of the global healthcare system. But they are facing the challenges of a relatively insufficient number [1] . In China, the total number of registered nurses is around 5.2 million, but the nursing workforce is far from sufficient to meet the growing demands for human health. Nursing staffing levels continue to cause concern. [2] Nurses are of the professionals who perform important activities in the hospital, while the shortage of nursing staff is not a new phenomenon. The State Nursing Organization surveyed the reasons for the nursing shortage and found that nurses are struggling in the workplace. [3] . Many nurses have experienced cumulative fatigue and burnout and ultimately choose to leave the healthcare industry, creating a vicious cycle of workforce shortages. As a result, there is growing concern about work-related fatigue among nurses. Fatigue is a state of mental or physical exhaustion caused by overwork. It is influenced by various factors, including physical factors related to modern industrial structures, environmental factors, and individuals' psychological factors. Work-related fatigue is defined as extreme tiredness and reduced functional capacity that is experienced during and at the end of the workday [4] . It is also defined as a relatively constant feeling of lack of interest and difficulty in concentrating on ongoing activities [5] . These feelings lead to a conscious effort to maintain or regain attention. It is well-documented that work-related fatigue of nurses is a safety risk for both the health of nurses and safety of patients. [6] It is a mentally, physically or emotionally exhausted state wrought by work, and there are numerous nurses are suffering. It not only affects the alert of nurses, but also increases the risk of medical errors [7] . Previous research has shown that fatigued nurses may experience various types of performance deficits, including slowed reaction time, memory lapses, difficulty concentrating, and reduced alertness [8] . However, to the best of our knowledge, it has not been comprehensively studied what the underlying mechanism for nurses' work-related fatigue is. Fatigue among nurses has been a long-standing issue [9] . Numerous factors can affect work-related fatigue, and especially nurses work across 24 hours of the day [10] . The 24Model was initially proposed in 2005, based on DT, SCM, LCM, and MMOS [11] . The 24Model can be applied in safety practice and to address fundamental issues in safety science. It can also demonstrate the paths of accident factors. The factors of events can be classified into two main categories: organizational factors and individual factors. Organizational factors can be further divided into organizational culture and management system, while individual factors can be divided into individual capability and individual acts and conditions. [11] This classification is known as the 24Model. As a result, 24Model can provide a framework for identifying fundamental issues in safety science and the development of safety research centers. Therefore, there is a need for research into the extent of nurses' work fatigue and its risk factors on the basis of 24 Model. Thus, this article aims to determine the prevalence of work-related fatigue in clinical nurses using meta-analytic and narrative synthesis methods. Additionally, it seeks to describe and summarize the factors associated with work-related fatigue. 2. Methods 2.1.Design This study was conducted according to the PRISMA guideline for the systematic review and meta-analysis [12, 13] . The protocol has been registered in PROSPERO with the registration number CRD42023456337. 2.2 Search method The search was carried out using 8 databases: PubMed, Web of Science, Scopus, CINAHL, PsycINFO, and Chinese Database, including China National Knowledge Infrastructure (CNKI), Chinese Biological Medical (CBM) and WanFang Database from the inception of the databases until October 2023. Due to the language proficiency of our research team, we focused solely on studies published in English and Chinese. The search was conducted with the three key terms: ‘work-related fatigue’, ‘nurse’ and ‘risk factor’. The search strategy in PubMed was as follows: (((clinical nurs*) OR (nurs*) OR (nursing staff)) AND ((work fatigue) OR (work-related fatigue) OR (workplace fatigue) OR (occupational fatigue) OR (tiredness) OR (chronic fatigue) OR (acute fatigue))) AND ((risk factor*) OR (hazard factor*) OR (dangerous factor*)). No grey literature search was performed. 2.3 Eligibility criteria The inclusion criteria were: (1)Study design: observational studies; (2)Population: registered nurses ; (3)Instrument: used the work-related fatigue questionnaires or scales as an instrument to measure the level of work-related fatigue; (4)Outcomes: reported the mean score of the instrument or the factors associated with work-related fatigue among nurses; (5)Language: English or Chinese; (6)Other: clearly reported independent data for nurses, although the total study sample was mixed with other healthcare workers. The exclusion criteria were: (1)Study design: case reports, review articles, conference abstracts, comments, letters to the editor and protocols; (2)Population: nurse managers, auxiliary nurses, and nurse educators; (3)Outcomes: without sufficient data; (4)Others: full text not available and low quality articles. 2.4 Study selection All the search results were imported into Endnote X9 software. After removing duplicates, studies were selected based on the title, abstract, and full text. The included studies were then fully read and any disagreements were resolved by consulting a third team member. 2.5 Data extraction The data was extracted using Excel tables and classified as the first author, publication year, country, study design, sampling method, sample size, age and instruments for the level of work-related fatigue among nurses. 2.6 Quality appraisal The quality appraisal of all included studies was conducted independently by two members of the research team. The Agency for Healthcare Research and Quality (AHRQ) was used to assess the quality of cross-sectional studies, while the Newcastle-Ottawa scale (NOS) was used for longitudinal studies. The AHRQ comprises 11 items, including study design, participants, variables, data, and bias. Scores of 0 were given for 'No' and 'Unclear', while a score of 1 was given for 'Yes'. The AHRQ scores were divided into three grades of quality: low (0–3), medium (4–7), and high (8–11) [14] . The NOS has 6 items in three groups (selection, exposure, and comparability) ranging from 0 to 9. Scores >7 showed high quality of studies [15] . Any disagreements in scores were resolved through mutual consultation with a third party. 2.7 Meta-analysis A meta-analysis was conducted to calculate the pooled mean scores of three subscales using Stata 18.0 software. Random effects models were used and the data were presented as pooled mean scores and weighted effect sizes with 95% confidence intervals. A sensitivity analysis was completed to figure out whether any of the studies in the meta-analysis produced changes in outcome. The Cochran Q test and the I 2 index were used to determine the heterogeneity of the sample. I 2 index of 25% showed low, 50% showed moderate, > 75% showed high [16] . Besides, publication bias was assessed using Egger's tests. 3. Results 3.1 Search results 3259 initial studies were searched among 8 databases, of which 821 were duplicates and removed. The titles and abstracts of 2438 articles were screened. At this step, 2358 papers were excluded. After screening based on the inclusion and exclusion criteria, 48 studies were finally included. The flowchart is shown in Fig. 1 according to PRISMA guidelines. 3.2 Characteristics of the included studies The 48 articles included a total of 30688 clinical nurses. The majority of the studies were cross-sectional studies and used convenience sampling. The articles included in this study were published from 2006 to 2013, of which 36(75%) were conducted in Asian countries (China, Korean, Saudi Arabia, Iran, Thailand, Jordan, Cyprus), 2 were conducted in European countries(Poland, Norway, France), 4 were conducted in Oceanian countries(Australia, New Zealand),6 were conducted in American countries(American, Chile, Distrito Federal).The quality ratings of the cross-sectional studies ranged from 4 to 8 according to the AQHR and the longitudinal studies ranged from 5 to 6 according to the NOS scale. The Table 1 presents the main characteristics of the 48 included studies. Table 1 Characteristics of the included studies Author(year) Country Study design Sampling method Sample size Age Mean (SD) instruments Quality scores of study Yang(2023) [17] China crosssectional random sampling 621 Not Reported Self-diagnostic Questionnaire on the Accumulation of Fatigue of Laborers High Zhu(2023) [18] China crosssectional convenience sampling 661 32.72 ± 7.63 Fatigue Scale-14,FS-14 Middle Chen(2023) [19] China crosssectional convenience sampling 446 25.34 ± 2.71 Occupational Fatigue Exhaustion Recovery scale (OFER) High Li(2022) [20] China crosssectional convenience sampling 70 36.24 ± 2.45 MBI-GS Middle Tang(2022) [21] China crosssectional convenience sampling 2918 Not Reported Self-diagnosis Checklist for Assessment of Workers’ Accumulated Fatigue High Daouda(2022) [22] France longitudinal study convenience sampling 695 Not Reported the Pichot Fatigue Scale Middle Lee(2022) [23] Korea crosssectional Snowball sampling 234 33.37 ± 8.34 the Fatigue Severity Scale (FSS) Middle Chang(2022) [24] China longitudinal study convenience sampling 196 26.7 ± 5.0 The Checklist Individual Strength (CIS)-Subjective feeling of fatigue Middle Alsayed(2022) [25] Saudi Arabia crosssectional convenience sampling 282 30.58 ± 6.33 The Occupational Fatigue Exhaustion Recovery (OFER) scale Middle Zhan(2020) [26] China crosssectional convenience sampling 2667 Not Reported Fatigue Scale-14 High Zhao(2022) [27] China crosssectional convenience sampling 68 26.3 ± 3.1 MBI-SS Middle Qian(2022) [28] China crosssectional convenience sampling 1122 31.41 ± 7.35 Occupational Fatigue Exhaustion Recovery scale (OFER) High Li(2022) [29] China crosssectional convenience sampling 551 32.26 ± 8.07 Occupational Fatigue Exhaustion Recovery scale (OFER) Middle Gan(2022) [30] China crosssectional convenience sampling 207 32.33 ± 6.47 fatigue assessment instrument, FAI Middle Alshammari(2022) [31] Saudi Arabian crosssectional convenience sampling 125 Not Reported Occupational Fatigue Exhaustion Recovery scale (OFER) Middle Ross(2021) [32] American crosssectional convenience sampling 313 Not Reported Occupational Fatigue Exhaustion Recovery (OFER) Middle Tung(2021) [33] China crosssectional convenience sampling 829 Not Reported Copenhagen burnout inventory Middle Hossein(2021) [34] Iran crosssectional random sampling 500 31.78 ± 6.89 Persian version of the Multi-dimensional Assessment of Fatigue (P-MAF) Scale Middle Zhao(2021) [35] China crosssectional convenience sampling 212 Not Reported occupational fatigue scale Middle Sun(2021) [36] China crosssectional convenience sampling 159 Not Reported Fatigue Assessment Instrument-FAI Middle Luo(2021) [37] China crosssectional convenience sampling 189 31.25 ± 5.55 occupational fatigue scale Middle Liu(2021) [38] China crosssectional convenience sampling 518 32.74 ± 8.26 Fatigue Scale-14 (FS-14) Middle Hong(2021) [39] Korea crosssectional convenience sampling 227 28.42 ± 4.17 Occupational Fatigue Exhaustion Recovery scale (OFER) Middle Zdanowicz (2020) [40] Poland crosssectional convenience sampling 134 Not Reported the Fatigue Severity Scale (FSS) Middle Poursadeqiyan (2020) [41] Iran crosssectional convenience sampling 143 30.75 ± 6.77 The Swedish Occupational Fatigue Inventory (SOFI) Middle Martínez (2020) [42] Chile crosssectional convenience sampling 110 Not Reported the Checklist Individual Strength (CIS) Middle Liu(2020) [43] China crosssectional convenience sampling 996 32.65 ± 7.35 the Chinese version of the Chalder Fatigue Scale (CFS) High Yang(2020) [44] China crosssectional convenience sampling 277 Not Reported fatigue self-assessment scale, FSAS Middle Qiu(2020) [45] China crosssectional convenience sampling 537 31.75 ± 10.26 Fatigue Scale-14,FS-14 Middle Gander(2019) [6] New Zealand crosssectional convenience sampling 3133 40(21–71) Four fatigue-related outcome questionaire Middle Jalilian(2019) [46] Iran crosssectional random sampling 522 29.5 ± 7.03 Multidimensional Fatigue Inventory (MFI) Middle Ismail(2019) [47] Jordan crosssectional convenience sampling 220 28.54 ± 3.78 Occupational Fatigue Exhaustion Recovery scale (OFER) Middle Yu(2019) [48] New Zealand crosssectional convenience sampling 67 38 ± 8.6 Occupational Fatigue Exhaustion Recovery scale (OFER) Middle Silva(2018) [49] Distrito Federal crosssectional convenience sampling 110 39.47 ± 9.17 the Brazilian version of the Need for Recovery Scale Middle Chaiard(2018) [50] Thailand crosssectional random sampling 220 Not Reported the Fatigue Questionnaire (FQ) developed by Chalder Middle Sagherian (2017) [51] American crosssectional convenience sampling 40 30.9 ± 7.86 Occupational Fatigue Exhaustion Recovery scale (OFER) Middle Sagherian (2017) [52] American crosssectional convenience sampling 77 Not Reported Occupational Fatigue Exhaustion Recovery scale (OFER) Middle Han(2014) [53] Korea crosssectional convenience sampling 80 Not Reported Occupational Fatigue Exhaustion Recovery scale (OFER) Middle Hazzard(2013) [54] American crosssectional convenience sampling 20 43.13 ± 9.45 Occupational Fatigue Exhaustion Recovery scale (OFER) Middle Fang(2012) [55] China crosssectional convenience sampling 581 29.49 ± 6.73 15-item Occupational Fatigue Exhaustion Recovery (OFER) scale Middle Raftopoulos (2012) [56] Cyprus crosssectional convenience sampling 1480 36.68 ± 10.39 a question Middle Liu(2011) [57] China crosssectional random sampling 744 29.38 ± 6.74 Fatigue Scale-14,FS-14 Middle Feng(2011) [58] China crosssectional convenience sampling 102 28.14 ± 5.19 Fatigue Scale-14,FS-14 Middle Zhu(2009) [59] China crosssectional convenience sampling 200 25.08 ± 5.83 fatigue assessment instrument, FAI Middle Samaha(2007) [60] Australia crosssectional convenience sampling 111 72.8 ± 24.4 The Checklist Individual Scale Middle Winwood (2006) [61] Australia crosssectional convenience sampling 846 Not Reported Occupational Fatigue Exhaustion Recovery scale (OFER) Middle Eriksen(2006) [62] Norway longitudinal study convenience sampling 5547 NR self-made questionaire Middle Fang(2006) [63] China crosssectional convenience sampling 581 29.49 ± 6.73 Occupational Fatigue Exhaustion Recovery scale (OFER) Middle 3.3 The measurements of nurses’ work-related fatigue Out of the 48 articles, 16 (29%) studies, which included 4917 clinical nurses, used the Occupational Fatigue Exhaustion Recovery scale (OFER) to measure the level of work-related fatigue among nurses. Among them, 14 studies in the meta-analysis included the appropriate data. Additionally, 6 (12.5%) studies used the FS-14. The Occupational Fatigue Exhaustion Recovery (OFER) subscale scores in the 14 studies are shown in Table 2 . Table 2 the scores of subscale in 14 studies Study sample AF mean AF sd sample CF mean CF sd sample IR mean IR sd Chen 2023 [19] 446 64.28 18.54 446 45.12 15.88 446 55.92 16.03 Alsayed 2022 [25] 282 57.01 17.12 282 52.27 23.19 282 50.6 13.08 Qian 2022 [28] 1122 63.81 18.9 1122 53.95 23.14 1122 44.09 15.93 Li 2022 [29] 551 53.33 22.24 551 42.95 24.24 551 51.58 19.7 Alshammari 2022 [31] 125 80.43 15.12 125 72.66 21.36 125 77.49 13.8 Ross 2021 [32] 313 NR NR 120 51.69 27.82 119 40.84 23.63 Hong 2021 [39] 227 68.63 17.81 227 64.51 15.16 227 42.88 18.66 Ismail 2019 [47] 220 61.63 27.17 220 57.18 17.41 220 56.25 17.39 Yu 2019 [48] 67 49.9 12.2 67 27 22.9 67 46.4 10.5 Sagherian 2017 [51] 40 66.11 18.66 40 41.67 24.45 40 43.89 21.15 Sagherian 2017 [52] 77 66.8 18.7 77 70.26 21.7 77 39.24 18.57 Hazzard 2013 [54] 20 66.5 19.3 20 35.7 17.2 20 52 18.6 Winwood 2006 [61] 846 58.87 21.65 846 51.9 23.91 846 37.1 23.32 Fang 2006 [63] 581 63.4 21.69 581 47.14 23.38 581 NR NR 3.4Meta-analysis 3.4.1 Mean scores on OFER subscales Of the16 studies used the Occupational Fatigue Exhaustion Recovery scale (OFER) to measure the work-related fatigue, 14 studies (n = 4917) in the meta- analysis included the appropriate data. There are three dimension of the OFER: acute fatigue(AF), chronic fatigue(CF) and inter-shift recovery(IR). The scoring of OFER items is based on a 7-point Likert scale ranging from 0 (strongly disagree) to 6 (strongly agree). The scoring method for each subscale is based on the following formula: sum (subscale items scores) /30 × 100, and the potential range of total scores for each subscale is 0–100 [64] . According to Winwood and colleagues, a score of 1–25 indicates a low level of construct for each subscale, while a score of 26–50 indicates a low to moderate level. A score of 51–75 suggests a moderate to high level, and a score of 76–100 indicates a high level [61] . The random-effects pooled mean score estimate for the AF was 62.99(95% CI: 59.38–66.4), CF was 51.30(95% CI: 46.82–55.78) and IR was 49.15(95% CI: 43.92–54.37). The results indicated that the average clinical nurses was at moderately high level of acute fatigue and chronic fatigue, while at low/moderate level of inter-shift recovery. And there was significant evidence of between-study heterogeneity(AF: Q = 435.92, df = 12, p = 0.000, I2 = 97.2%; CF: Q = 643.74, df = 13, p = 0.000, I2 = 98.0%; IR: Q = 1057.28, df = 12, p = 0.000, I2 = 98.9%). The forest plots are shown in Fig. 2 – 4 . 3.4.2 Publication bias and Sensitivity analysis The results obtained from Egger’s test demonstrated that there was no statistically significant publication bias. The results obtained in this respect were 0.36 for AF (p = 0.727), 0.09 for CF (p = 0.930) and 0.52 for IR (p = 0.61). In our sensitivity analysis, when each of the studies in turn was eliminated from the analysis, the pooled mean scores or prevalence values did not change significantly. 3.5Related factors of work-related fatigue In order to provide a comprehensive overview of the factors related to work-related fatigue, the 24-Model is employed. This model offers a more accurate classification framework, thereby facilitating the implementation of effective workplace safety management strategies. There are four dimensionalities: organizational culture, management system, individual capability and individual acts and conditions [65] 3.5.1 organizational culture Sixteen studies demonstrated a significant relationship between organizational culture and work-related fatigue among clinical nurses. Nurses were found to have the worst work situation with lower scores for colleague support [18, 32] , manager support [22, 55, 62] and perceived organizational support [20, 25, 27, 28, 43, 53] . Studies found decision latitude (control) [32, 46] in jobs led to an increase in motivation and activity. This means that if nurses have more control over the task, they will be more motivated to carry out their responsibilities [46] . Safety climate is a subset of the organizational safety culture and represents a tangible manifestation of the safety culture in practice. There is a correlation between occupational fatigue and the safety climate [31, 41] . The work environment emerged as a significant predictor of high levels of nurse’ fatigue [21, 22, 57] . 3.5.2 management system Twenty-seven studies indicated a relationship between nursing management systems and work-related fatigue among clinical nurses. This was mainly associated with working hours [17, 29, 54] , daily overtime [20, 28, 52] , shift work pattern [25, 32, 44, 53, 57, 61] , night shift interval [6, 39, 50] , and average number of weekly night shifts [26, 40, 48] . A correlation was observed between work-related fatigue and the following variables: effort-reward balance [31, 43] , gross monthly income [35] , and rewards for well-done work [62] . Work demands, psychologic job demands, quantitative demands, occupational task [38] , and working intensity [45] were also found to be associated with work-related fatigue. Furthermore, occupational exposure to sexual harassment, physical violence, threats of violence, and bullying has also been associated with work-related fatigue [47, 55, 63] . 3.5.3 individual capability Twenty-six studies demonstrated that individual capability plays a role in work-related fatigue among nurses. There was a strong relationship between work experience and work-related fatigue, especially 5–10 years of nursing work experience [21, 24, 28, 45, 48, 50] . In addition, Seniority and position were statistically significant in relation to a high risk of personal and work-related fatigue [33] . Furthermore, job control [26, 28, 54, 59, 62] , coping strategy [6, 30, 46, 58] , individual coping resources [38] , individual stress response [38] , resilience [35, 43] and emotional intelligence [36, 37] were also found to be associated with work-related fatigue. Nurses are frequently required to perform multiple roles, including that of a caregiver [32] , which can lead to work-family role conflict and subsequent fatigue [25, 29] . It is also evident that physical and mental health play a significant role throughout the occupational lifetime [19, 26, 31, 47, 57] . 3.5.4 individual acts and conditions Thirty studies showed that individual acts and conditions had an impact on nurses’ work-related fatigue. The demographic data of age [35, 38] , marital status [45, 50, 62] , gender [21, 31, 40] , fertility circumstance [22] and qualifications [58] demonstrated a correlation with work-related fatigue. There was a relationship between an unhealthy lifestyle (eating, drinking, exercising) [25, 26, 48, 57, 60] and work-related fatigue. Job absenteeism was related to fatigue [22, 42] . Poor sleep conditions, insomnia and sleepiness were found to be associated with severe fatigue [28, 32, 52, 54, 63] . A bad mood, such as anxiety and depression, was found to be associated with fatigue [23] . Lower job satisfaction was found to be associated with work-related fatigue [30, 34, 37, 44, 47, 53, 56] . 4. Discussion To the best of our knowledge, no systematic review and meta-analysis has been published that focuses on the level and risk factors of work-related fatigue among clinical nurses. The phenomenon of nurses’ work-related fatigue is crucial in today’s 7/24h healthcare system. We conducted this meta-analysis and systematic review to: (1) evaluate the level of work-related fatigue among clinical nurses; (2) systematically identify the factors associated with work-related fatigue using the 24-Model. The Occupational Fatigue Exhaustion Recovery (OFER) questionnaire consists of 15 items rated on a 7-point Likert scale, ranging from strongly disagree (0) to strongly agree (6). The questionnaire includes three subscales: chronic fatigue (items 1–5), acute fatigue (items 6–10), and inter-shift recovery (items 11–15). Respondents' scores can be interpreted as low (0–25), low-moderate (26–50), moderate-high (51–75), and high (76–100) [64] . Thus, the study demonstrated that clinical nurses exhibited moderate-high levels of acute and chronic fatigue, as well as low-moderate levels of inter-shift recovery. This underscores the pervasiveness of acute and chronic fatigue as an occupational issue among clinical nurses. Due to extended working hours, heavy workloads and other factors, nurses experience mental and physical fatigue that is challenging to alleviate [66] . Nevertheless, the recovery period between shifts is insufficient to restore nurses to their optimal working state [67] . Research has demonstrated that the level of work fatigue among clinical nurses varies within and between shifts, and builds up after consecutive shifts [68] . Insufficient recovery, coupled with high-intensity work demands and the absence of relief and reduction in work fatigue among clinical nurses, can result in an increasing level of work fatigue, which in turn can lead to a reduction in work happiness, an increase in nurse turnover intention, and an elevated risk of patient safety incidents [69] . The adverse effects of work fatigue among clinical nurses have a profound impact on their individual physical and mental health, patient safety, and the overall working atmosphere and environment of the organization [69] . The identification and detection of work fatigue are of paramount importance in the management of clinical nurse fatigue. Currently, there is no unified standard for measuring work fatigue among clinical nurses, with various types of questionnaires or scales in use [4, 71, 72] . In this meta-analysis, data from 14 articles using the OFER questionnaire were used to classify work fatigue into acute fatigue, chronic fatigue, and inter-shift fatigue, which was also widely accepted by most studies. However, some scholars have classified work fatigue into physical fatigue, mental fatigue, and emotional fatigue [4] . Furthermore, we observed considerable heterogeneity not only in the measurements employed but also in the subscale scores among the studies. The measurement of different tools may result in varying degrees of bias in the research results. Consequently, further exploration and research are required to identify and monitor work fatigue in clinical nurses. In the post-epidemic era, it is of the utmost importance that the impact of various risk factors on nurses' health be subjected to a more systematic and comprehensive evaluation. It is essential to classify these factors in a systematic and explicit manner. In doing so, it is important to distinguish between occupational and non-occupational factors. The 24-Model provided an accurate classification framework for us, advocating the concept of “from organization to individual”. This is divided into four subcategories: organizational culture, management system, personal capability and individual act. [11] . Organizational culture may be defined as the set of norms, values, and basic assumptions that drive both the quality of work life and the quality of care within an organization. [73] . In the current context of mounting pressure within the healthcare industry, it is imperative that we advocate for the transformation of a culture of fatigue into a culture of health. [74] . The findings of the study indicate that positive relationships with colleagues, sufficient social support, a positive work environment, a positive safety atmosphere, perceived organizational support, and fair and impartial superiors play an indispensable role in reducing the work fatigue of nurses. [75] Consequently, nursing managers are encouraged to foster the creation of an optimal organizational culture environment and to provide a supportive working atmosphere for clinical nurses. The management of shift work, the daily overload of workload, the remuneration received in the form of salary, the number of hours worked each month, and the nature of the work itself all contribute to the level of work fatigue experienced by clinical nurses [22, 23, 34] . The term ‘management system’ is used in this context to describe a system of management practices, policies and procedures related to shift work, personnel salary management, and other aspects that are relevant to the organization’s operations. [76] . The quantity of shift work, the duration of working hours, the extent of the workload, and the recovery period between shifts can have a considerable impact on the rest and sleep patterns of nurses [77] . This, in turn, is linked to the phenomenon of work fatigue. In light of the aforementioned considerations, it is recommended that hospitals and nursing managers allocate human resources in a reasonable manner, establish a fair compensation mechanism, and implement a fatigue risk management system [78–80] . A conceptual model of multi-level fatigue risk management in the nursing work system was proposed by another researcher based on the hierarchical fatigue risk management model in the fatigue risk management system [79] . This model is founded upon empirical evidence and is informed by systems engineering methodologies and informatics. System engineers and clinical analysts can transform data related to recorded nursing intensity and other needs in the work environment into meaningful information to guide nursing leaders in monitoring, decision-making, and developing strategies for managing employee fatigue. A number of personal factors can influence the onset of work fatigue. These include work experience, work control, coping strategies, health status, professional title and status, family function, and psychological stress resistance [81] . Consequently, hospitals and nursing managers should prioritize the provision of educational programs designed to mitigate occupational fatigue among employees [78] . These programs should cultivate the capacity of nurses to proactively prevent and respond to fatigue. 5. Limitations It should be noted that there are certain limitations to this study. Firstly, studies published in English and Chinese were included, which may have resulted in the exclusion of studies published in other languages. Secondly, there is considerable heterogeneity across studies. Thirdly, despite the availability of other scales, meta-analysis only included studies that used the OFER scale (the most extensively used) to assess nurses’ work-related fatigue. Furthermore, the self-reported approach may introduce recall bias, resulting in the loss of data obtained from other tools used to measure work-related fatigue. Fourthly, the results can only explain the relevant relationship and cannot be used to infer the causal relationship. Finally, the present findings may be limited in scope and should be interpreted with caution. 6. Conclusion In conclusion, the findings of this systematic review and meta-analysis indicate that nurses working in clinical settings experience moderate levels of work-related fatigue. The findings suggest that hospital and nursing supervisors should place greater emphasis on the management of nurses’ work-related fatigue. The results of this study indicate the necessity for further research to identify the variables, including organizational and individual characteristics, that have the greatest impact on the onset of work-related fatigue. This could provide a theoretical basis for the management and prevention of nurses’ work-related fatigue. Abbreviations PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses AF acute fatigue CF chronic fatigue IR inter-shift recovery Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The inquiries should be directed to the corresponding author, and data from this study will be made available upon reasonable request. Competing interests The authors declare that they have no competing interests Funding This work was financially supported by Research on Humanities and Social Sciences by the Ministry of Education(21YJA630049). Authors' contributions R. P and YF. L made equal contributions to this manuscript. R. P, YF. L, SY. L and Y. W designed this study; R. P and YF. L ran the search strategy; R. P, Z. D and Y. C selected articles and extracted data; R. P, ZH. H and YL. W evaluate the quality of the evidence; R. P and YF. L wrote the manuscript; WJ. L, ZX. H, R.Y SY. L and Y. W review and edited it. SY. L and Y. W supervision, and funding acquisition. Acknowledgements We would like to thank all the authors of our included studies. References MENG Z, ZHANG L, ZAN H, et al. Psychological resilience and work engagement of Chinese nurses: a chain mediating model of career identity and quality of work life[J]. Front Psychol, 2023,14: 1275511. LIU J, LIU X, ZHENG J, et al. Changes over 10 years in the nursing workforce in Guangdong province, China: Three-wave multisite surveys[J]. J Nurs Manag, 2021,29(8): 2630-2638. What’s Really Behind the Nursing Shortage? 1,500 Nurses Share Their Stories[EB/OL]. (2022-10-10)[2022-12-02]. https://nurse.org/articles/nursing-shortage-study/. FRONE M R, TIDWELL M O. The meaning and measurement of work fatigue: Development and evaluation of the Three-Dimensional Work Fatigue Inventory (3D-WFI)[J]. 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STEEGE L M, PINEKENSTEIN B J, RAINBOW J G, et al. Addressing Occupational Fatigue in Nurses: Current State of Fatigue Risk Management in Hospitals, Part 2[J]. J Nurs Adm, 2017,47(10): 484-490. HIESTAND S, FORTHUN I, WAAGE S, et al. Associations between excessive fatigue and pain, sleep, mental-health and work factors in Norwegian nurses[J]. PLoS One, 2023,18(4): e282734. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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18:47:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103951,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study selection process\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4334355/v1/2935c053ab00b0a20fce947d.png"},{"id":57442546,"identity":"7d57fe20-6cb8-4855-9e2b-7d00f17dfdcf","added_by":"auto","created_at":"2024-05-30 18:47:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70810,"visible":true,"origin":"","legend":"\u003cp\u003ePooled mean scores by subscale: Acute fatigue\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4334355/v1/03557ab63d5ecf1ceea8c142.png"},{"id":57442544,"identity":"3cdf14e9-fab8-4dc2-94db-5e1db1166880","added_by":"auto","created_at":"2024-05-30 18:47:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":82049,"visible":true,"origin":"","legend":"\u003cp\u003ePooled mean scores by subscale: Chronic fatigue\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4334355/v1/16d4c43924f9d63d6f399e2e.png"},{"id":57442545,"identity":"35361997-a85f-4a0a-a38c-15dc3df945ba","added_by":"auto","created_at":"2024-05-30 18:47:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":76240,"visible":true,"origin":"","legend":"\u003cp\u003ePooled mean scores by subscale: Inter-shift recovery\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4334355/v1/e36d61607636be4e54a1619f.png"},{"id":57747499,"identity":"72a4c669-4019-4998-9d13-ad47d28a1e14","added_by":"auto","created_at":"2024-06-05 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Introduction","content":"\u003cp\u003eNurses are the largest group of healthcare professionals and a vital part of the global healthcare system. But they are facing the challenges of a relatively insufficient number\u003csup\u003e[1]\u003c/sup\u003e. In China, the total number of registered nurses is around 5.2\u0026nbsp;million, but the nursing workforce is far from sufficient to meet the growing demands for human health. Nursing staffing levels continue to cause concern.\u003csup\u003e[2]\u003c/sup\u003e Nurses are of the professionals who perform important activities in the hospital, while the shortage of nursing staff is not a new phenomenon. The State Nursing Organization surveyed the reasons for the nursing shortage and found that nurses are struggling in the workplace. \u003csup\u003e[3]\u003c/sup\u003e. Many nurses have experienced cumulative fatigue and burnout and ultimately choose to leave the healthcare industry, creating a vicious cycle of workforce shortages. As a result, there is growing concern about work-related fatigue among nurses.\u003c/p\u003e \u003cp\u003eFatigue is a state of mental or physical exhaustion caused by overwork. It is influenced by various factors, including physical factors related to modern industrial structures, environmental factors, and individuals' psychological factors. Work-related fatigue is defined as extreme tiredness and reduced functional capacity that is experienced during and at the end of the workday\u003csup\u003e[4]\u003c/sup\u003e. It is also defined as a relatively constant feeling of lack of interest and difficulty in concentrating on ongoing activities\u003csup\u003e[5]\u003c/sup\u003e. These feelings lead to a conscious effort to maintain or regain attention. It is well-documented that work-related fatigue of nurses is a safety risk for both the health of nurses and safety of patients.\u003csup\u003e[6]\u003c/sup\u003e It is a mentally, physically or emotionally exhausted state wrought by work, and there are numerous nurses are suffering. It not only affects the alert of nurses, but also increases the risk of medical errors\u003csup\u003e[7]\u003c/sup\u003e. Previous research has shown that fatigued nurses may experience various types of performance deficits, including slowed reaction time, memory lapses, difficulty concentrating, and reduced alertness\u003csup\u003e[8]\u003c/sup\u003e. However, to the best of our knowledge, it has not been comprehensively studied what the underlying mechanism for nurses' work-related fatigue is.\u003c/p\u003e \u003cp\u003eFatigue among nurses has been a long-standing issue\u003csup\u003e[9]\u003c/sup\u003e. Numerous factors can affect work-related fatigue, and especially nurses work across 24 hours of the day\u003csup\u003e[10]\u003c/sup\u003e. The 24Model was initially proposed in 2005, based on DT, SCM, LCM, and MMOS\u003csup\u003e[11]\u003c/sup\u003e. The 24Model can be applied in safety practice and to address fundamental issues in safety science. It can also demonstrate the paths of accident factors. The factors of events can be classified into two main categories: organizational factors and individual factors. Organizational factors can be further divided into organizational culture and management system, while individual factors can be divided into individual capability and individual acts and conditions.\u003csup\u003e[11]\u003c/sup\u003e This classification is known as the 24Model. As a result, 24Model can provide a framework for identifying fundamental issues in safety science and the development of safety research centers.\u003c/p\u003e \u003cp\u003eTherefore, there is a need for research into the extent of nurses' work fatigue and its risk factors on the basis of 24 Model. Thus, this article aims to determine the prevalence of work-related fatigue in clinical nurses using meta-analytic and narrative synthesis methods. Additionally, it seeks to describe and summarize the factors associated with work-related fatigue.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1.Design\u003c/h2\u003e \u003cp\u003eThis study was conducted according to the PRISMA guideline for the systematic review and meta-analysis\u003csup\u003e[12, 13]\u003c/sup\u003e. The protocol has been registered in PROSPERO with the registration number CRD42023456337.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Search method\u003c/h2\u003e \u003cp\u003eThe search was carried out using 8 databases: PubMed, Web of Science, Scopus, CINAHL, PsycINFO, and Chinese Database, including China National Knowledge Infrastructure (CNKI), Chinese Biological Medical (CBM) and WanFang Database from the inception of the databases until October 2023. Due to the language proficiency of our research team, we focused solely on studies published in English and Chinese.\u003c/p\u003e \u003cp\u003eThe search was conducted with the three key terms: \u0026lsquo;work-related fatigue\u0026rsquo;, \u0026lsquo;nurse\u0026rsquo; and \u0026lsquo;risk factor\u0026rsquo;. The search strategy in PubMed was as follows: (((clinical nurs*) OR (nurs*) OR (nursing staff)) AND ((work fatigue) OR (work-related fatigue) OR (workplace fatigue) OR (occupational fatigue) OR (tiredness) OR (chronic fatigue) OR (acute fatigue))) AND ((risk factor*) OR (hazard factor*) OR (dangerous factor*)). No grey literature search was performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Eligibility criteria\u003c/h2\u003e \u003cp\u003eThe inclusion criteria were: (1)Study design: observational studies; (2)Population: registered nurses ; (3)Instrument: used the work-related fatigue questionnaires or scales as an instrument to measure the level of work-related fatigue; (4)Outcomes: reported the mean score of the instrument or the factors associated with work-related fatigue among nurses; (5)Language: English or Chinese; (6)Other: clearly reported independent data for nurses, although the total study sample was mixed with other healthcare workers.\u003c/p\u003e \u003cp\u003eThe exclusion criteria were: (1)Study design: case reports, review articles, conference abstracts, comments, letters to the editor and protocols; (2)Population: nurse managers, auxiliary nurses, and nurse educators; (3)Outcomes: without sufficient data; (4)Others: full text not available and low quality articles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Study selection\u003c/h2\u003e \u003cp\u003eAll the search results were imported into Endnote X9 software. After removing duplicates, studies were selected based on the title, abstract, and full text. The included studies were then fully read and any disagreements were resolved by consulting a third team member.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data extraction\u003c/h2\u003e \u003cp\u003eThe data was extracted using Excel tables and classified as the first author, publication year, country, study design, sampling method, sample size, age and instruments for the level of work-related fatigue among nurses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Quality appraisal\u003c/h2\u003e \u003cp\u003eThe quality appraisal of all included studies was conducted independently by two members of the research team. The Agency for Healthcare Research and Quality (AHRQ) was used to assess the quality of cross-sectional studies, while the Newcastle-Ottawa scale (NOS) was used for longitudinal studies. The AHRQ comprises 11 items, including study design, participants, variables, data, and bias. Scores of 0 were given for 'No' and 'Unclear', while a score of 1 was given for 'Yes'. The AHRQ scores were divided into three grades of quality: low (0\u0026ndash;3), medium (4\u0026ndash;7), and high (8\u0026ndash;11)\u003csup\u003e[14]\u003c/sup\u003e. The NOS has 6 items in three groups (selection, exposure, and comparability) ranging from 0 to 9. Scores \u0026gt;7 showed high quality of studies\u003csup\u003e[15]\u003c/sup\u003e. Any disagreements in scores were resolved through mutual consultation with a third party.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Meta-analysis\u003c/h2\u003e \u003cp\u003eA meta-analysis was conducted to calculate the pooled mean scores of three subscales using Stata 18.0 software. Random effects models were used and the data were presented as pooled mean scores and weighted effect sizes with 95% confidence intervals. A sensitivity analysis was completed to figure out whether any of the studies in the meta-analysis produced changes in outcome. The Cochran Q test and the I\u003csup\u003e2\u003c/sup\u003e index were used to determine the heterogeneity of the sample. I\u003csup\u003e2\u003c/sup\u003e index of 25% showed low, 50% showed moderate, \u0026gt;\u0026thinsp;75% showed high\u003csup\u003e[16]\u003c/sup\u003e. Besides, publication bias was assessed using Egger's tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Search results\u003c/h2\u003e \u003cp\u003e3259 initial studies were searched among 8 databases, of which 821 were duplicates and removed. The titles and abstracts of 2438 articles were screened. At this step, 2358 papers were excluded. After screening based on the inclusion and exclusion criteria, 48 studies were finally included. The flowchart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e according to PRISMA guidelines.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Characteristics of the included studies\u003c/h2\u003e \u003cp\u003eThe 48 articles included a total of 30688 clinical nurses. The majority of the studies were cross-sectional studies and used convenience sampling. The articles included in this study were published from 2006 to 2013, of which 36(75%) were conducted in Asian countries (China, Korean, Saudi Arabia, Iran, Thailand, Jordan, Cyprus), 2 were conducted in European countries(Poland, Norway, France), 4 were conducted in Oceanian countries(Australia, New Zealand),6 were conducted in American countries(American, Chile, Distrito Federal).The quality ratings of the cross-sectional studies ranged from 4 to 8 according to the AQHR and the longitudinal studies ranged from 5 to 6 according to the NOS scale. The Table\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the main characteristics of the 48 included studies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the included studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor(year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudy design\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSampling method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003einstruments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQuality scores of study\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYang(2023)\u003csup\u003e[17]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erandom sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelf-diagnostic Questionnaire on the Accumulation of Fatigue of Laborers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhu(2023)\u003csup\u003e[18]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.72\u0026thinsp;\u0026plusmn;\u0026thinsp;7.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFatigue Scale-14,FS-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen(2023)\u003csup\u003e[19]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.34\u0026thinsp;\u0026plusmn;\u0026thinsp;2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOccupational Fatigue Exhaustion Recovery scale (OFER)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi(2022)\u003csup\u003e[20]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMBI-GS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTang(2022)\u003csup\u003e[21]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelf-diagnosis Checklist for Assessment of Workers\u0026rsquo; Accumulated Fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaouda(2022)\u003csup\u003e[22]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elongitudinal study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ethe Pichot Fatigue Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLee(2022)\u003csup\u003e[23]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKorea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSnowball sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.37\u0026thinsp;\u0026plusmn;\u0026thinsp;8.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ethe Fatigue Severity Scale (FSS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChang(2022)\u003csup\u003e[24]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elongitudinal study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe Checklist Individual Strength (CIS)-Subjective feeling of fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlsayed(2022)\u003csup\u003e[25]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaudi Arabia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.58\u0026thinsp;\u0026plusmn;\u0026thinsp;6.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe Occupational Fatigue Exhaustion Recovery (OFER) scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhan(2020)\u003csup\u003e[26]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFatigue Scale-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhao(2022)\u003csup\u003e[27]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMBI-SS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQian(2022)\u003csup\u003e[28]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.41\u0026thinsp;\u0026plusmn;\u0026thinsp;7.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOccupational Fatigue Exhaustion Recovery scale (OFER)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi(2022)\u003csup\u003e[29]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.26\u0026thinsp;\u0026plusmn;\u0026thinsp;8.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOccupational Fatigue Exhaustion Recovery scale (OFER)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGan(2022)\u003csup\u003e[30]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.33\u0026thinsp;\u0026plusmn;\u0026thinsp;6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003efatigue assessment instrument, FAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlshammari(2022)\u003csup\u003e[31]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaudi Arabian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOccupational Fatigue Exhaustion Recovery scale (OFER)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoss(2021)\u003csup\u003e[32]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmerican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOccupational Fatigue Exhaustion Recovery (OFER)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTung(2021)\u003csup\u003e[33]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCopenhagen burnout inventory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHossein(2021)\u003csup\u003e[34]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erandom sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.78\u0026thinsp;\u0026plusmn;\u0026thinsp;6.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePersian version of the Multi-dimensional Assessment of Fatigue (P-MAF) Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhao(2021)\u003csup\u003e[35]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eoccupational fatigue scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSun(2021)\u003csup\u003e[36]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFatigue Assessment Instrument-FAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuo(2021)\u003csup\u003e[37]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.25\u0026thinsp;\u0026plusmn;\u0026thinsp;5.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eoccupational fatigue scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu(2021)\u003csup\u003e[38]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.74\u0026thinsp;\u0026plusmn;\u0026thinsp;8.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFatigue Scale-14 (FS-14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHong(2021)\u003csup\u003e[39]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKorea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.42\u0026thinsp;\u0026plusmn;\u0026thinsp;4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOccupational Fatigue Exhaustion Recovery scale (OFER)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZdanowicz\u003c/p\u003e \u003cp\u003e(2020)\u003csup\u003e[40]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ethe Fatigue Severity Scale (FSS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoursadeqiyan\u003c/p\u003e \u003cp\u003e(2020)\u003csup\u003e[41]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.75\u0026thinsp;\u0026plusmn;\u0026thinsp;6.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe Swedish Occupational Fatigue Inventory (SOFI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMart\u0026iacute;nez\u003c/p\u003e \u003cp\u003e(2020)\u003csup\u003e[42]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ethe Checklist Individual Strength (CIS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu(2020)\u003csup\u003e[43]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.65\u0026thinsp;\u0026plusmn;\u0026thinsp;7.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ethe Chinese version of the Chalder Fatigue Scale (CFS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYang(2020)\u003csup\u003e[44]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003efatigue self-assessment scale, FSAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQiu(2020)\u003csup\u003e[45]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.75\u0026thinsp;\u0026plusmn;\u0026thinsp;10.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFatigue Scale-14,FS-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGander(2019)\u003csup\u003e[6]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNew Zealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40(21\u0026ndash;71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFour fatigue-related outcome questionaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJalilian(2019)\u003csup\u003e[46]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erandom sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMultidimensional Fatigue Inventory (MFI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsmail(2019)\u003csup\u003e[47]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJordan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOccupational Fatigue Exhaustion Recovery scale (OFER)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYu(2019)\u003csup\u003e[48]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNew Zealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOccupational Fatigue Exhaustion Recovery scale (OFER)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilva(2018)\u003csup\u003e[49]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistrito Federal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.47\u0026thinsp;\u0026plusmn;\u0026thinsp;9.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ethe Brazilian version of the Need for Recovery Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChaiard(2018)\u003csup\u003e[50]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThailand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erandom sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ethe Fatigue Questionnaire (FQ) developed by Chalder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSagherian\u003c/p\u003e \u003cp\u003e(2017)\u003csup\u003e[51]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmerican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOccupational Fatigue Exhaustion Recovery scale (OFER)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSagherian\u003c/p\u003e \u003cp\u003e(2017)\u003csup\u003e[52]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmerican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOccupational Fatigue Exhaustion Recovery scale (OFER)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan(2014)\u003csup\u003e[53]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKorea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOccupational Fatigue Exhaustion Recovery scale (OFER)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHazzard(2013)\u003csup\u003e[54]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmerican\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.13\u0026thinsp;\u0026plusmn;\u0026thinsp;9.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOccupational Fatigue Exhaustion Recovery scale (OFER)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFang(2012)\u003csup\u003e[55]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.49\u0026thinsp;\u0026plusmn;\u0026thinsp;6.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15-item Occupational Fatigue Exhaustion Recovery (OFER) scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRaftopoulos\u003c/p\u003e \u003cp\u003e(2012)\u003csup\u003e[56]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCyprus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.68\u0026thinsp;\u0026plusmn;\u0026thinsp;10.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ea question\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu(2011)\u003csup\u003e[57]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003erandom sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.38\u0026thinsp;\u0026plusmn;\u0026thinsp;6.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFatigue Scale-14,FS-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeng(2011)\u003csup\u003e[58]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.14\u0026thinsp;\u0026plusmn;\u0026thinsp;5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFatigue Scale-14,FS-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhu(2009)\u003csup\u003e[59]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.08\u0026thinsp;\u0026plusmn;\u0026thinsp;5.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003efatigue assessment instrument, FAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSamaha(2007)\u003csup\u003e[60]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.8\u0026thinsp;\u0026plusmn;\u0026thinsp;24.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThe Checklist Individual Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWinwood\u003c/p\u003e \u003cp\u003e(2006)\u003csup\u003e[61]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot Reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOccupational Fatigue Exhaustion Recovery scale (OFER)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEriksen(2006)\u003csup\u003e[62]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elongitudinal study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eself-made questionaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFang(2006)\u003csup\u003e[63]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecrosssectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econvenience sampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.49\u0026thinsp;\u0026plusmn;\u0026thinsp;6.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOccupational Fatigue Exhaustion Recovery scale (OFER)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The measurements of nurses\u0026rsquo; work-related fatigue\u003c/h2\u003e \u003cp\u003eOut of the 48 articles, 16 (29%) studies, which included 4917 clinical nurses, used the Occupational Fatigue Exhaustion Recovery scale (OFER) to measure the level of work-related fatigue among nurses. Among them, 14 studies in the meta-analysis included the appropriate data. Additionally, 6 (12.5%) studies used the FS-14. The Occupational Fatigue Exhaustion Recovery (OFER) subscale scores in the 14 studies are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ethe scores of subscale in 14 studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003esample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAF mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAF sd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003esample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCF mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCF sd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003esample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIR mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIR sd\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen 2023\u003csup\u003e[19]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e55.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlsayed 2022\u003csup\u003e[25]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e50.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQian 2022\u003csup\u003e[28]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi 2022\u003csup\u003e[29]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlshammari 2022\u003csup\u003e[31]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e77.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoss 2021\u003csup\u003e[32]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHong 2021\u003csup\u003e[39]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsmail 2019\u003csup\u003e[47]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e56.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYu 2019\u003csup\u003e[48]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSagherian 2017\u003csup\u003e[51]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSagherian 2017\u003csup\u003e[52]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHazzard 2013\u003csup\u003e[54]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWinwood 2006\u003csup\u003e[61]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFang 2006\u003csup\u003e[63]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4Meta-analysis\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Mean scores on OFER subscales\u003c/h2\u003e \u003cp\u003eOf the16 studies used the Occupational Fatigue Exhaustion Recovery scale (OFER) to measure the work-related fatigue, 14 studies (n\u0026thinsp;=\u0026thinsp;4917) in the meta- analysis included the appropriate data. There are three dimension of the OFER: acute fatigue(AF), chronic fatigue(CF) and inter-shift recovery(IR). The scoring of OFER items is based on a 7-point Likert scale ranging from 0 (strongly disagree) to 6 (strongly agree). The scoring method for each subscale is based on the following formula: sum (subscale items scores) /30 \u0026times; 100, and the potential range of total scores for each subscale is 0\u0026ndash;100\u003csup\u003e[64]\u003c/sup\u003e. According to Winwood and colleagues, a score of 1\u0026ndash;25 indicates a low level of construct for each subscale, while a score of 26\u0026ndash;50 indicates a low to moderate level. A score of 51\u0026ndash;75 suggests a moderate to high level, and a score of 76\u0026ndash;100 indicates a high level\u003csup\u003e[61]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe random-effects pooled mean score estimate for the AF was 62.99(95% CI: 59.38\u0026ndash;66.4), CF was 51.30(95% CI: 46.82\u0026ndash;55.78) and IR was 49.15(95% CI: 43.92\u0026ndash;54.37). The results indicated that the average clinical nurses was at moderately high level of acute fatigue and chronic fatigue, while at low/moderate level of inter-shift recovery. And there was significant evidence of between-study heterogeneity(AF: Q\u0026thinsp;=\u0026thinsp;435.92, df\u0026thinsp;=\u0026thinsp;12, p\u0026thinsp;=\u0026thinsp;0.000, I2\u0026thinsp;=\u0026thinsp;97.2%; CF: Q\u0026thinsp;=\u0026thinsp;643.74, df\u0026thinsp;=\u0026thinsp;13, p\u0026thinsp;=\u0026thinsp;0.000, I2\u0026thinsp;=\u0026thinsp;98.0%; IR: Q\u0026thinsp;=\u0026thinsp;1057.28, df\u0026thinsp;=\u0026thinsp;12, p\u0026thinsp;=\u0026thinsp;0.000, I2\u0026thinsp;=\u0026thinsp;98.9%). The forest plots are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Publication bias and Sensitivity analysis\u003c/h2\u003e \u003cp\u003eThe results obtained from Egger\u0026rsquo;s test demonstrated that there was no statistically significant publication bias. The results obtained in this respect were 0.36 for AF (p\u0026thinsp;=\u0026thinsp;0.727), 0.09 for CF (p\u0026thinsp;=\u0026thinsp;0.930) and 0.52 for IR (p\u0026thinsp;=\u0026thinsp;0.61). In our sensitivity analysis, when each of the studies in turn was eliminated from the analysis, the pooled mean scores or prevalence values did not change significantly.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5Related factors of work-related fatigue\u003c/h2\u003e \u003cp\u003eIn order to provide a comprehensive overview of the factors related to work-related fatigue, the 24-Model is employed. This model offers a more accurate classification framework, thereby facilitating the implementation of effective workplace safety management strategies. There are four dimensionalities: organizational culture, management system, individual capability and individual acts and conditions \u003csup\u003e[65]\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 organizational culture\u003c/h2\u003e \u003cp\u003eSixteen studies demonstrated a significant relationship between organizational culture and work-related fatigue among clinical nurses. Nurses were found to have the worst work situation with lower scores for colleague support\u003csup\u003e[18, 32]\u003c/sup\u003e, manager support\u003csup\u003e[22, 55, 62]\u003c/sup\u003e and perceived organizational support\u003csup\u003e[20, 25, 27, 28, 43, 53]\u003c/sup\u003e. Studies found decision latitude (control)\u003csup\u003e[32, 46]\u003c/sup\u003e in jobs led to an increase in motivation and activity. This means that if nurses have more control over the task, they will be more motivated to carry out their responsibilities\u003csup\u003e[46]\u003c/sup\u003e. Safety climate is a subset of the organizational safety culture and represents a tangible manifestation of the safety culture in practice. There is a correlation between occupational fatigue and the safety climate\u003csup\u003e[31, 41]\u003c/sup\u003e. The work environment emerged as a significant predictor of high levels of nurse\u0026rsquo; fatigue\u003csup\u003e[21, 22, 57]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 management system\u003c/h2\u003e \u003cp\u003eTwenty-seven studies indicated a relationship between nursing management systems and work-related fatigue among clinical nurses. This was mainly associated with working hours\u003csup\u003e[17, 29, 54]\u003c/sup\u003e, daily overtime\u003csup\u003e[20, 28, 52]\u003c/sup\u003e, shift work pattern\u003csup\u003e[25, 32, 44, 53, 57, 61]\u003c/sup\u003e, night shift interval\u003csup\u003e[6, 39, 50]\u003c/sup\u003e, and average number of weekly night shifts\u003csup\u003e[26, 40, 48]\u003c/sup\u003e. A correlation was observed between work-related fatigue and the following variables: effort-reward balance\u003csup\u003e[31, 43]\u003c/sup\u003e, gross monthly income\u003csup\u003e[35]\u003c/sup\u003e, and rewards for well-done work\u003csup\u003e[62]\u003c/sup\u003e. Work demands, psychologic job demands, quantitative demands, occupational task\u003csup\u003e[38]\u003c/sup\u003e, and working intensity\u003csup\u003e[45]\u003c/sup\u003e were also found to be associated with work-related fatigue. Furthermore, occupational exposure to sexual harassment, physical violence, threats of violence, and bullying has also been associated with work-related fatigue\u003csup\u003e[47, 55, 63]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3 individual capability\u003c/h2\u003e \u003cp\u003eTwenty-six studies demonstrated that individual capability plays a role in work-related fatigue among nurses. There was a strong relationship between work experience and work-related fatigue, especially 5\u0026ndash;10 years of nursing work experience\u003csup\u003e[21, 24, 28, 45, 48, 50]\u003c/sup\u003e. In addition, Seniority and position were statistically significant in relation to a high risk of personal and work-related fatigue\u003csup\u003e[33]\u003c/sup\u003e. Furthermore, job control\u003csup\u003e[26, 28, 54, 59, 62]\u003c/sup\u003e, coping strategy\u003csup\u003e[6, 30, 46, 58]\u003c/sup\u003e, individual coping resources\u003csup\u003e[38]\u003c/sup\u003e, individual stress response\u003csup\u003e[38]\u003c/sup\u003e, resilience\u003csup\u003e[35, 43]\u003c/sup\u003e and emotional intelligence\u003csup\u003e[36, 37]\u003c/sup\u003e were also found to be associated with work-related fatigue. Nurses are frequently required to perform multiple roles, including that of a caregiver\u003csup\u003e[32]\u003c/sup\u003e, which can lead to work-family role conflict and subsequent fatigue\u003csup\u003e[25, 29]\u003c/sup\u003e. It is also evident that physical and mental health play a significant role throughout the occupational lifetime\u003csup\u003e[19, 26, 31, 47, 57]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.5.4 individual acts and conditions\u003c/h2\u003e \u003cp\u003eThirty studies showed that individual acts and conditions had an impact on nurses\u0026rsquo; work-related fatigue. The demographic data of age\u003csup\u003e[35, 38]\u003c/sup\u003e, marital status\u003csup\u003e[45, 50, 62]\u003c/sup\u003e, gender\u003csup\u003e[21, 31, 40]\u003c/sup\u003e, fertility circumstance\u003csup\u003e[22]\u003c/sup\u003e and qualifications\u003csup\u003e[58]\u003c/sup\u003e demonstrated a correlation with work-related fatigue. There was a relationship between an unhealthy lifestyle (eating, drinking, exercising)\u003csup\u003e[25, 26, 48, 57, 60]\u003c/sup\u003e and work-related fatigue. Job absenteeism was related to fatigue\u003csup\u003e[22, 42]\u003c/sup\u003e. Poor sleep conditions, insomnia and sleepiness were found to be associated with severe fatigue\u003csup\u003e[28, 32, 52, 54, 63]\u003c/sup\u003e. A bad mood, such as anxiety and depression, was found to be associated with fatigue\u003csup\u003e[23]\u003c/sup\u003e. Lower job satisfaction was found to be associated with work-related fatigue\u003csup\u003e[30, 34, 37, 44, 47, 53, 56]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo the best of our knowledge, no systematic review and meta-analysis has been published that focuses on the level and risk factors of work-related fatigue among clinical nurses. The phenomenon of nurses\u0026rsquo; work-related fatigue is crucial in today\u0026rsquo;s 7/24h healthcare system. We conducted this meta-analysis and systematic review to: (1) evaluate the level of work-related fatigue among clinical nurses; (2) systematically identify the factors associated with work-related fatigue using the 24-Model.\u003c/p\u003e \u003cp\u003eThe Occupational Fatigue Exhaustion Recovery (OFER) questionnaire consists of 15 items rated on a 7-point Likert scale, ranging from strongly disagree (0) to strongly agree (6). The questionnaire includes three subscales: chronic fatigue (items 1\u0026ndash;5), acute fatigue (items 6\u0026ndash;10), and inter-shift recovery (items 11\u0026ndash;15). Respondents' scores can be interpreted as low (0\u0026ndash;25), low-moderate (26\u0026ndash;50), moderate-high (51\u0026ndash;75), and high (76\u0026ndash;100)\u003csup\u003e[64]\u003c/sup\u003e. Thus, the study demonstrated that clinical nurses exhibited moderate-high levels of acute and chronic fatigue, as well as low-moderate levels of inter-shift recovery. This underscores the pervasiveness of acute and chronic fatigue as an occupational issue among clinical nurses.\u003c/p\u003e \u003cp\u003eDue to extended working hours, heavy workloads and other factors, nurses experience mental and physical fatigue that is challenging to alleviate\u003csup\u003e[66]\u003c/sup\u003e. Nevertheless, the recovery period between shifts is insufficient to restore nurses to their optimal working state\u003csup\u003e[67]\u003c/sup\u003e. Research has demonstrated that the level of work fatigue among clinical nurses varies within and between shifts, and builds up after consecutive shifts\u003csup\u003e[68]\u003c/sup\u003e. Insufficient recovery, coupled with high-intensity work demands and the absence of relief and reduction in work fatigue among clinical nurses, can result in an increasing level of work fatigue, which in turn can lead to a reduction in work happiness, an increase in nurse turnover intention, and an elevated risk of patient safety incidents\u003csup\u003e[69]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe adverse effects of work fatigue among clinical nurses have a profound impact on their individual physical and mental health, patient safety, and the overall working atmosphere and environment of the organization\u003csup\u003e[69]\u003c/sup\u003e. The identification and detection of work fatigue are of paramount importance in the management of clinical nurse fatigue. Currently, there is no unified standard for measuring work fatigue among clinical nurses, with various types of questionnaires or scales in use\u003csup\u003e[4, 71, 72]\u003c/sup\u003e. In this meta-analysis, data from 14 articles using the OFER questionnaire were used to classify work fatigue into acute fatigue, chronic fatigue, and inter-shift fatigue, which was also widely accepted by most studies. However, some scholars have classified work fatigue into physical fatigue, mental fatigue, and emotional fatigue\u003csup\u003e[4]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, we observed considerable heterogeneity not only in the measurements employed but also in the subscale scores among the studies. The measurement of different tools may result in varying degrees of bias in the research results. Consequently, further exploration and research are required to identify and monitor work fatigue in clinical nurses.\u003c/p\u003e \u003cp\u003eIn the post-epidemic era, it is of the utmost importance that the impact of various risk factors on nurses' health be subjected to a more systematic and comprehensive evaluation. It is essential to classify these factors in a systematic and explicit manner. In doing so, it is important to distinguish between occupational and non-occupational factors. The 24-Model provided an accurate classification framework for us, advocating the concept of \u0026ldquo;from organization to individual\u0026rdquo;. This is divided into four subcategories: organizational culture, management system, personal capability and individual act.\u003csup\u003e[11]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOrganizational culture may be defined as the set of norms, values, and basic assumptions that drive both the quality of work life and the quality of care within an organization.\u003csup\u003e[73]\u003c/sup\u003e. In the current context of mounting pressure within the healthcare industry, it is imperative that we advocate for the transformation of a culture of fatigue into a culture of health.\u003csup\u003e[74]\u003c/sup\u003e. The findings of the study indicate that positive relationships with colleagues, sufficient social support, a positive work environment, a positive safety atmosphere, perceived organizational support, and fair and impartial superiors play an indispensable role in reducing the work fatigue of nurses.\u003csup\u003e[75]\u003c/sup\u003e Consequently, nursing managers are encouraged to foster the creation of an optimal organizational culture environment and to provide a supportive working atmosphere for clinical nurses.\u003c/p\u003e \u003cp\u003eThe management of shift work, the daily overload of workload, the remuneration received in the form of salary, the number of hours worked each month, and the nature of the work itself all contribute to the level of work fatigue experienced by clinical nurses\u003csup\u003e[22, 23, 34]\u003c/sup\u003e. The term \u0026lsquo;management system\u0026rsquo; is used in this context to describe a system of management practices, policies and procedures related to shift work, personnel salary management, and other aspects that are relevant to the organization\u0026rsquo;s operations.\u003csup\u003e[76]\u003c/sup\u003e. The quantity of shift work, the duration of working hours, the extent of the workload, and the recovery period between shifts can have a considerable impact on the rest and sleep patterns of nurses\u003csup\u003e[77]\u003c/sup\u003e. This, in turn, is linked to the phenomenon of work fatigue.\u003c/p\u003e \u003cp\u003eIn light of the aforementioned considerations, it is recommended that hospitals and nursing managers allocate human resources in a reasonable manner, establish a fair compensation mechanism, and implement a fatigue risk management system\u003csup\u003e[78\u0026ndash;80]\u003c/sup\u003e. A conceptual model of multi-level fatigue risk management in the nursing work system was proposed by another researcher based on the hierarchical fatigue risk management model in the fatigue risk management system\u003csup\u003e[79]\u003c/sup\u003e. This model is founded upon empirical evidence and is informed by systems engineering methodologies and informatics. System engineers and clinical analysts can transform data related to recorded nursing intensity and other needs in the work environment into meaningful information to guide nursing leaders in monitoring, decision-making, and developing strategies for managing employee fatigue.\u003c/p\u003e \u003cp\u003eA number of personal factors can influence the onset of work fatigue. These include work experience, work control, coping strategies, health status, professional title and status, family function, and psychological stress resistance\u003csup\u003e[81]\u003c/sup\u003e. Consequently, hospitals and nursing managers should prioritize the provision of educational programs designed to mitigate occupational fatigue among employees\u003csup\u003e[78]\u003c/sup\u003e. These programs should cultivate the capacity of nurses to proactively prevent and respond to fatigue.\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eIt should be noted that there are certain limitations to this study. Firstly, studies published in English and Chinese were included, which may have resulted in the exclusion of studies published in other languages. Secondly, there is considerable heterogeneity across studies. Thirdly, despite the availability of other scales, meta-analysis only included studies that used the OFER scale (the most extensively used) to assess nurses\u0026rsquo; work-related fatigue. Furthermore, the self-reported approach may introduce recall bias, resulting in the loss of data obtained from other tools used to measure work-related fatigue. Fourthly, the results can only explain the relevant relationship and cannot be used to infer the causal relationship. Finally, the present findings may be limited in scope and should be interpreted with caution.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eIn conclusion, the findings of this systematic review and meta-analysis indicate that nurses working in clinical settings experience moderate levels of work-related fatigue. The findings suggest that hospital and nursing supervisors should place greater emphasis on the management of nurses\u0026rsquo; work-related fatigue. The results of this study indicate the necessity for further research to identify the variables, including organizational and individual characteristics, that have the greatest impact on the onset of work-related fatigue. This could provide a theoretical basis for the management and prevention of nurses\u0026rsquo; work-related fatigue.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePRISMA \u0026nbsp;Preferred Reporting Items for Systematic Reviews and Meta-Analyses\u003c/p\u003e\n\u003cp\u003eAF \u0026nbsp; \u0026nbsp; \u0026nbsp; acute fatigue\u003c/p\u003e\n\u003cp\u003eCF \u0026nbsp; \u0026nbsp; \u0026nbsp; chronic fatigue\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;inter-shift recovery\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe inquiries should be directed to the corresponding author, and data from this study will be made available upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by Research on Humanities and Social Sciences by the Ministry of Education(21YJA630049).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR. P and YF. L made equal contributions to this manuscript. R. P, YF. L, SY. L and Y. W designed this study; R. P and YF. L ran the search strategy; R. P, Z. D and Y. C selected articles and extracted data; R. P, ZH. H and YL. W evaluate the quality of the evidence; R. P and YF. L wrote the manuscript; WJ. L, ZX. H, R.Y SY. L and Y. W review and edited it. SY. L and Y. W supervision, and funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all the authors of our included studies.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMENG Z, ZHANG L, ZAN H, et al. Psychological resilience and work engagement of Chinese nurses: a chain mediating model of career identity and quality of work life[J]. Front Psychol, 2023,14: 1275511.\u003c/li\u003e\n\u003cli\u003eLIU J, LIU X, ZHENG J, et al. 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International journal of environmental research and public health, 2021,18(15): 7953.\u003c/li\u003e\n\u003cli\u003eZDANOWICZ T, TUROWSKI K, CELEJ-SZUSTER J, et al. Insomnia, Sleepiness, and Fatigue Among Polish Nurses[J]. Workplace health \u0026amp; safety, 2020,68(6): 272-278.\u003c/li\u003e\n\u003cli\u003ePOURSADEQIYAN M, AREFI M, KHALEGHI S, et al. Investigation of the relationship between the safety climate and occupational fatigue among the nurses of educational hospitals in Zabol[J]. Journal of Education and Health Promotion, 2020,9(1): 238.\u003c/li\u003e\n\u003cli\u003eMART\u0026Iacute;NEZ-ZARAGOZA F, FERN\u0026Aacute;NDEZ-CASTRO J, BENAVIDES-GIL G, et al. How the Lagged and Accumulated Effects of Stress, Coping, and Tasks Affect Mood and Fatigue during Nurses' Shifts[J]. International journal of environmental research and public health, 2020,17(19): 7277.\u003c/li\u003e\n\u003cli\u003eLIU L, WU D, WANG L, et al. 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Improving fatigue risk management in healthcare: A scoping review of sleep-related/ fatigue-management interventions for nurses and midwives (reprint)[J]. Int J Nurs Stud, 2020,112: 103745.\u003c/li\u003e\n\u003cli\u003eSTEEGE L M, PINEKENSTEIN B. Addressing Occupational Fatigue in Nurses: A Risk Management Model for Nurse Executives[J]. The Journal of nursing administration, 2016,46(4): 193-200.\u003c/li\u003e\n\u003cli\u003eSTEEGE L M, PINEKENSTEIN B J, RAINBOW J G, et al. Addressing Occupational Fatigue in Nurses: Current State of Fatigue Risk Management in Hospitals, Part 2[J]. J Nurs Adm, 2017,47(10): 484-490.\u003c/li\u003e\n\u003cli\u003eHIESTAND S, FORTHUN I, WAAGE S, et al. Associations between excessive fatigue and pain, sleep, mental-health and work factors in Norwegian nurses[J]. PLoS One, 2023,18(4): e282734.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Work-related fatigue, Risky factors, Prevalence, Clinical nurses, Meta-analysis, Systematic review","lastPublishedDoi":"10.21203/rs.3.rs-4334355/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4334355/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNumerous studies have evaluated the level of work-related fatigue in nurses and its risk factors. However, none of these studies have rigorously assessed these through meta-analysis and systematic review.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis study aimed to examine work-related fatigue among nurses and identify the factors that influence it.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThe review searched eight databases, including PubMed, Web of Science, Scopus, CINAHL, PsycINFO, and Chinese databases such as China National Knowledge Infrastructure (CNKI), Chinese Biological Medical (CBM), and WanFang Database. The temporal scope of the search encompasses a temporal interval commencing from inception and extending to October 2023. The PRISMA guideline was used to report the meta-analysis and systematic review. The research team conducted a comprehensive study of selection, quality assessments, data extraction and analysis of all included literature. The means and standard deviations of three subscales of work-related fatigue were pooled using random effects meta-analysis using Stata 18.0 software. The registration PROSPERO number is CRD42023456337.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 3259 initial studies were retrieved, of which 48 qualified articles were finally included in this article. The pooled mean scores of acute fatigue(AF), chronic fatigue(CF) and inter-shift recovery(IR) was 62.99(95% CI: 59.38\u0026ndash;66.4), 51.30(95% CI: 46.82\u0026ndash;55.78) and 49.15(95% CI: 43.92\u0026ndash;54.37). Furthermore, the factors influencing nurses\u0026rsquo; work-related fatigue included both organizational and individual variables.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eClinical nurses were at a moderately high level of acute and chronic fatigue. Four themes were summarized as factors influencing the level of work-related fatigue among nurses, including organizational culture, Management system, individual capability and individual acts. Consequently, Nurse\u0026rsquo; work-related fatigue is a vital global issue that roots in multiple causes, and more resources should be used to improve current situation.\u003c/p\u003e","manuscriptTitle":"The prevalence and factors of work-related fatigue among nurses: a meta-analysis and systematic review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-30 18:47:10","doi":"10.21203/rs.3.rs-4334355/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"944cafb1-2ec7-46ec-b0c1-045c5da5e8b3","owner":[],"postedDate":"May 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-05T06:01:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-30 18:47:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4334355","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4334355","identity":"rs-4334355","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}