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Psychometric analysis of work organization and fatigue instruments and their relationship with occupational accidents: a structural equation modeling approach

BMC Health Services Research volume 25, Article number: 239 (2025) Cite this article

Abstract

Background

Work organization significantly impacts occupational incidents and fatigue in hospital settings, particularly among nurses. This study aimed to evaluate the psychometric properties of instruments measuring work organization and fatigue and to examine their relationship with occupational accidents.

Methods

A cross-sectional study was conducted in 2019 with 200 nurses working in hospitals in Qom, Iran using the stratified sampling method. Data were collected using three standardized tools: the Work Organization Questionnaire, the Fatigue Checklist, and a demographic information questionnaire. Structural equation modeling was employed to analyze the data, while instrument validity and reliability were assessed through Cronbach’s alpha, composite reliability, and average variance extracted (AVE). Analysis was performed using Smart PLS and SPSS V20.

Results

The analysis revealed a significant relationship between work organization and occupational accidents (t = 3.22, p < 0.05). However, the relationships between work organization and fatigue (t = 0.03) and between fatigue and occupational accidents (t = 1.49) were not statistically significant. The Work Organization Questionnaire (WOAQ) demonstrated robust validity and reliability, making it suitable for assessing occupational risks in hospital environments. In contrast, the Fatigue Questionnaire (CIS) exhibited acceptable validity but insufficient reliability (Cronbach’s alpha < 0.7), highlighting the need for further refinement.

Conclusion

This study revealed that the Work Organization Questionnaire has acceptable validity and reliability, making it suitable for hospital settings, while the Fatigue Questionnaire requires further revision. It is recommended that hospital administrators optimize work schedules and provide fatigue management training, and policymakers utilize validated tools to reduce occupational risks and enhance workplace safety.

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Introduction

Work organization and job stressors are shaped by the competitive landscape that employers face in the global economy [1]. Work organization is defined as the method by which work is structured, supervised, and processed. It reflects the objective nature of the work process [2] and refers to the way work is designed and executed [3]. According to Wall and Clegg, work organization "typically represents a broader perspective than job design, explicitly linking jobs to their organizational context" [4]. Key aspects of work organization include payment systems, task-level work content, management practices, and the psychosocial work environment, such as job demands, work control, social support, and effort-reward imbalance [1, 5].

In recent decades, significant changes in work organization have occurred [6]. The transformation and growth of organizations have underscored the importance of work organization factors as potential risk elements [7]. Changes such as flexible and lean production technologies, flatter management structures, and nontraditional job methods have fundamentally altered work organization, raising concerns about their potential negative impacts on workers' health and safety [7, 8]. Work organization influences physical exposures at the workplace, including workload, work pace, work schedules, rest cycles, equipment and workstation design, product design, and environmental design. It also affects the psychosocial work environment, including job security ambiguity [2], job demands, work control, social support, and effort-reward imbalance [5], which can lead to various stress and strain reactions [2].

Among technical, human, operational, and organizational factors influencing the sequence of occupational accidents [9], organizational factors play a major role [10]. Organizational and managerial deficiencies are critical causes of human errors, directly or indirectly leading to catastrophic accidents [11]. This highlights the potential value of work organization in understanding and preventing injuries and safety violations [10].

Additionally, various studies have reported associations between adverse aspects of work organization and psychological distress [12], depression [13, 14], psychosocial variables [15], circadian rhythm disorders [2], musculoskeletal disorders [2, 15,16,17,18], fatigue, stress, decreased performance, undesirable health behaviors, and acute and chronic physical disorders [5]. Fatigue, in particular, is a critical factor in safety violations and accidents. Williamson et al. [19] demonstrated that fatigue impairs vigilance, decision-making, and response times, thereby increasing the likelihood of accidents. Recent studies also emphasize the mediating and moderating effects of organizational factors, such as perceived safety climate, on the relationship between fatigue and safety behavior [20].

In the context of work organization and its impact on psychological outcomes, Sarani et al. highlighted the significance of emotional intelligence and self-efficacy in mitigating stress and enhancing performance among administrative staff [21]. These findings suggest that individual psychological factors, shaped by work organization, can significantly influence overall job outcomes.

Moreover, organizational strategies are not only linked to individual performance but also to broader economic outcomes. Hadian et al., in their systematic review, demonstrated how the economic dimensions of medical tourism are influenced by efficient organizational practices [22]. This underscores the broader relevance of work organization in shaping operational and economic dynamics within healthcare and related industries.

In the context of work organization and fatigue, Bielić et al. found that transitioning to automation reduced human creativity and staff numbers while increasing workloads for faster operations. These changes in work organization compounded fatigue, a critical factor in human error, accounting for 75% to 95% of marine accidents. Other studies similarly highlight the impact of work organization dimensions on fatigue [23,24,25,26].

Fatigue is a common complaint among workers, with approximately 20% reporting symptoms [27, 28]. It disrupts physical and cognitive performance [29] and contributes to accidents, injuries, and deaths across various safety–critical settings, including transportation and healthcare [30]. Fatigue prevalence is particularly high among nurses [31,32,33,34], who constitute the largest group of healthcare professionals [35, 36]. Fatigue reduces the ability to process hazardous situations and respond appropriately [37]. This increases the likelihood of errors in clinical judgment or medication administration, affecting patient outcomes and imposing significant costs on healthcare systems [31, 38].

Annually, approximately 1.3 million patients are harmed due to errors during hospitalization, with over 100,000 preventable deaths from adverse events [39]. Common accidents among nurses include those involving biological materials, needles, and sharp objects [40, 41]. The Texas Health Institute ranks hospitals as one of the five most hazardous occupational environments [42].

Nurse fatigue results from factors such as high job demands, long work shifts [32, 33], rotating schedules, poor sleep quality [33, 43], work structures [44], and workload [31]. Its consequences include reduced satisfaction, increased turnover, high injury and burnout rates, poor patient outcomes, decreased motivation, longer reaction times, impaired concentration, memory issues, and increased error rates [32, 41, 44, 45]. Fatigue has been statistically linked to accidents and injuries in many studies, often identified as a contributing factor [37, 46]. Therefore, the consequences of fatigue in nurses have an impact at individual, organizational, and social levels [47], which This has led to its recognition as a national concern for both nurse and patient safety [29, 48].

To comprehensively understand how work organization, fatigue, and accidents influence each other, this study was conducted to evaluate their relationships among nurses in Qom, Iran, in 2019.

Hypotheses

  • H1: The psychometric properties (reliability and validity) of the instruments measuring work organization and fatigue meet acceptable standards for occupational health research.

  • H2: Work organization is significantly associated with occupational accidents among nurses.

  • H3: Work organization is significantly associated with fatigue among nurses.

  • H4: Fatigue is significantly associated with occupational accidents among nurses.

  • H5: Fatigue mediates the relationship between work organization and occupational accidents.

Methods

This descriptive-analytical cross-sectional study was conducted in 2019 to examine the relationship between work organization, fatigue, and accidents among 200 nurses working in hospitals in Qom, Iran. Participants were selected using stratified sampling. Data were collected through self-reported questionnaires completed by the participants after receiving detailed explanations about the study. The data included quantitative, qualitative, and short-answer responses, obtained using three standardized questionnaires: the Work Organization Assessment Questionnaire (WOAQ) [49], the Checklist Individual Strength (CIS) [50], and a demographic information questionnaire. The inclusion criteria were be in good general health, have no physical or psychological disorders, have no secondary employment, and have at least one year of professional experience. The exclusion criterion was a lack of willingness to continue participation at any stage of the study. Participants were thoroughly briefed on the study process, and informed consent was obtained prior to their involvement.

Measurement tools

Demographic questionnaire

The demographic questionnaire included information on participants' characteristics, such as: Age, Gender, Marital status, Work experience, Education level, Number of training courses and accidents. In this questionnaire, data regarding the number of occupational accidents were collected. These accidents included cases such as needle stick injuries and sharps-related accidents, falls and exposure to hazardous materials. To reduce bias, participants were encouraged to report accidents honestly and without concern for judgment.

Fatigue questionnaire

To assess participants' fatigue, the standardized 20-item Checklist Individual Strength (CIS) questionnaire was used, which includes four subscales: Subjective Feeling of Fatigue (8 items), reduction in concentration (5 items), motivation reduction (4 items), and physical activity reduction (3 items). The questionnaire used a 7-point Likert scale ranging from 0 to 6 (from "never" to "always"). Participants reported their feelings over the past two weeks.

For scoring, responses to questions 2, 5, 6, 7, 8, 11, 12, 15, and 20 were scored on a 1 to 7 scale (from "never" to "always"), while responses to questions 1, 3, 4, 9, 10, 13, 14, 16, 17, 18, and 19 were reverse scored on the same scale. Beurskens et al. reported good internal consistency and test–retest reliability for this questionnaire (Cronbach’s alpha = 0.9) [50]. In the study by Khondan et al., the Cronbach's alpha for the Persian version of the questionnaire was reported to be 0.67 [51]. Total CIS scores were computed by summing item scores across its four subscales. Higher scores on the CIS reflected greater levels of fatigue.

Work Organization Assessment Questionnaire (WOAQ)

The Work Organization Assessment Questionnaire (WOAQ), a standardized 28-item questionnaire, was used to assess work organization. This questionnaire evaluates potential inherent risks in job design and management using five subscales: quality of relationships with management (9 items), reward and recognition (7 items), workload issues (4 items), quality of relationships with colleagues (2 items), and quality of physical environment (6 items). Participants were asked to assess each aspect of their work in terms of how problematic or good it was over the past six months, using a 5-point Likert scale (5 = very good, 1 = major problem). The assessment was based on the participants' knowledge and experience.

The questionnaire uses a situational rather than psychological approach. Instead of asking participants, "How much do you feel upset or stressed about this aspect of job design (or management)?" it asks, "How good or poor do you and your colleagues think this aspect of job design (or management) is?" This approach allows for the identification of both positive and negative features of job design and management. Griffiths et al. reported good internal consistency and test–retest reliability for this questionnaire (Cronbach’s alpha = 0.9) [49].

At the time of this study, the WOAQ had not been officially validated in Iranian settings. This research aimed to address this gap by conducting a psychometric evaluation of the Persian version of the WOAQ.

In this study, to determine the validity and reliability of the Work Organization Assessment Questionnaire, the questionnaire was first translated into Persian for cultural adaptation by relevant experts along with an English expert. After obtaining a unified translation through consensus, it was then back-translated into English by a native English speaker fluent in Persian. The translated version was then compared with the original version to ensure qualitative consistency and accuracy. Following this process, a finalized Persian version of the questionnaire was created.

Finally, the validity and reliability of the questionnaire were assessed. Structural validity was evaluated using exploratory factor analysis on 60% of the sample members, who were randomly selected. The translated version of the questionnaire was then validated using confirmatory factor analysis on the remaining 40% of the sample, which had not been used in the exploratory analysis.

Additionally, reliability was examined by assessing internal consistency using Cronbach’s alpha coefficient. The total score WOAQ was calculated by summing the scores of its subscales. Higher scores indicated better organizational conditions.

The questionnaires were distributed completely anonymously to ensure the confidentiality and privacy of all participants. No personally identifiable information (such as names, identification numbers, or other traceable details) was collected as part of the responses.

The questionnaires were completed manually without requiring participants to disclose any personal details. Once collected, the data were anonymized and coded for analysis to eliminate any possibility of identifying individual participants.

Data analysis methods

Partial Least Squares Structural Equation Modeling (PLS-SEM) was used for data analysis. This method was chosen due to its capability to handle complex models, accommodate moderate sample sizes, and operate without strict normality assumptions. For Measurement Model Assessment, The psychometric properties of the instruments were evaluated by examining Cronbach’s alpha, Composite Reliability (CR), and Average Variance Extracted (AVE).

And for Structural Model Assessment, Hypothesized relationships between variables were tested using path coefficients, t-values, and R2 values. The analyses were conducted using software SmartPLS. PLS was selected due to the exploratory nature of the study and the need to simultaneously assess the instruments and the relationships between variables.

Results

The results obtained from the relevant descriptive statistical tests for the demographic variables of the study participants are presented in Table 1. The findings showed that the majority of nurses were female (74.5%), married (54.4%), had a bachelor’s degree or higher (91%), and worked in shifts (84.6%).

Table 1 Demographic characteristics participants (n = 200)
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Based on the T-values obtained from confirmatory factor analysis in the initial analyses, it was found that questions 4 and 6 of the Work Organization Questionnaire and questions 3, 5, 7, 10, 15, and 17 of the Fatigue Questionnaire did not have significant relationships with the evaluated indicators. Therefore, these items were removed for further analysis. After revising the questions, the analysis results for determining two reliability criteria—Cronbach’s alpha and composite reliability—showed that the Cronbach’s alpha values, except for the Quality of Relationships with Colleagues index, and the composite reliability values for all dimensions of the Work Organization Questionnaire were above the acceptable minimum of 0.7, indicating good internal consistency [52]. For the Fatigue Questionnaire, the Cronbach’s alpha values for all domains were below the minimum acceptable value of 0.7, with only the Subjective Feeling of Fatigue domain showing a composite reliability above 0.7.

To assess convergent validity, the Average Variance Extracted (AVE) index, which indicates the ability of a dimension’s indicators to explain that dimension, was used. The threshold for an acceptable AVE value is 0.4 [53]. The results showed that all AVE values for the indicators of both the Work Organization and Fatigue questionnaires were above 0.4, confirming that the convergent validity of the questionnaires is at an acceptable level (Table 2).

Table 2 Convergent validity and reliability of factors (n = 200)
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To further evaluate the reliability of the constructs, factor loadings were assessed in addition to reporting Cronbach’s alpha and composite reliability. For the Work Organization Questionnaire, the factor loadings were above 0.4 for all items. For the Fatigue Questionnaire, factor loadings exceeded 0.4 for all items except questions 2, 6, 8, 11, and 20. These results indicate that this criterion is met satisfactorily (Fig. 1).

Fig. 1
figure 1

The executed model with factor loadings

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The results of the analyses conducted to assess discriminant validity, through comparing the square root of the AVE of each indicator with the correlation coefficients between the indicators, showed that the correlation of each indicator with itself was higher than with other indicators. These results confirm the satisfactory discriminant validity of the questionnaires (Table 3).

Table 3 The square root of the AVE for each indicator and the correlation coefficients between the indicators (n = 200)
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The T-values from confirmatory factor analysis at a 95% confidence level, with values exceeding the minimum T-value for the significance of standardized coefficients (1.96) [50], were used to assess the significance of the relationships between the items of each questionnaire and the evaluated variable. The results the T-values for all items were greater than 1.96.

The results of the analyses conducted to examine the relationships between the variables, as shown in Fig. 2 and Table 4, indicate that the relationships in the model have been confirmed. However, the relationships between fatigue and accidents and work organization and fatigue were rejected, as the T-values obtained for these relationships were less than 1.96.

Fig. 2
figure 2

The executed model with T-values for the significance of coefficients

Full size image
Table 4 T-values and the impact of variables on each other (n = 200)
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The obtained Goodness-of-Fit (GOF) value of 0.659, which is greater than the acceptable threshold of 0.4, indicates a good fit of the model.

The relationships between the main variables Work Organization, Fatigue, and Incidents were analyzed using path coefficients, T-values, and P-values. The results are summarized in Table 5.

Table 5 Path coefficients for main variables
Full size table

The results of the parametric and non-parametric statistical analyses conducted to examine the relationships between the demographic variables and fatigue, accidents, and work organization among nurses are presented in Table 6.

Table 6 Correlation of demographic characteristics the studied variables (n = 200)
Full size table

Discussion

Work organization, by defining the characteristics, links, and relationships between work systems, plays a critical role in achieving organizational balance and reducing negative human and organizational outcomes [54]. Therefore, having an effective tool to assess work organization and its negative consequences, such as fatigue—a significant factor contributing to accidents [30]—is essential, particularly since fatigue is prevalent among nurses [31,32,33]. A common challenge in comprehensive risk assessment studies is the length and complexity of the questionnaires, or their inability to effectively identify the nature of the risks in a workplace environment. The Work Organization Assessment Questionnaire (WOAQ), with its concise yet comprehensive content, effectively addresses these challenges [55].

Thus, this study aimed to evaluate the psychometric properties of the Persian version of the WOAQ as a tool for identifying and measuring work-related risks, especially in healthcare organizations, and to assess the relationship between work organization, fatigue, and occupational accidents among nurses in hospitals in Qom.

Griffiths et al., in their study on the development and validation of the WOAQ, used exploratory factor analysis with Varimax rotation to confirm the 28-item structure with five dimensions: Quality of Physical Environment, Quality of Relationships with Colleagues, Quality of Relationships with Management, Reward and Recognition, and Workload Issues. They found the questionnaire to have good test–retest reliability and internal consistency, with an acceptable Cronbach’s alpha of 0.9 [49]. In the present study, after performing preliminary confirmatory factor analysis, revising and removing items with factor loadings below the acceptable level of 0.4, the final WOAQ consisted of 26 items across five dimensions. The overall Cronbach’s alpha of 0.93 and composite reliability of 0.98 indicated good internal consistency and reliability. Similarly, Karimi et al. [55] reported a high internal consistency (Rho = 0.94) for the WOAQ, confirming it as a valid and reliable scale in the Australian nursing community.

In this study, the convergent validity of the WOAQ was confirmed based on the AVE values for all five dimensions, which were above the acceptable threshold of 0.4. Additionally, discriminant validity was confirmed, as the square root of the AVE for each dimension was higher than the correlations between that dimension and other dimensions. Karimi et al. [56] further confirmed the validity of the WOAQ across healthcare organizations through a metric invariance test.

For evaluating the validity and reliability of the Persian version of the Fatigue Questionnaire, the original Checklist Individual Strength (CIS) was used, which consists of 20 items across four dimensions. This tool has been widely reported as valid for measuring fatigue in working populations [50]. After confirmatory factor analysis, the final version of the Fatigue Questionnaire included 14 items and three dimensions: Concentration, Motivation, and Subjective Feeling of Fatigue. The Cronbach’s alpha for the Persian version was 0.51, which is below the acceptable threshold, while the composite reliability (1.03) exceeded the acceptable level. Previous studies, such as those by Ergin et al. [57] and Aratake et al. [58], reported higher Cronbach’s alpha values (0.87 and 0.91, respectively), indicating good reliability. However, in our study, confirmatory factor analysis did not show an ideal model fit. Worm-Smeitink et al. [59] reported good internal consistency (Cronbach’s alpha = 0.84–0.95) and test–retest reliability (r = 0.74–0.86) for the 20-item CIS and moderate to high correlations with other fatigue scales.

The results of the confirmatory factor analysis, with T-values greater than 1.96 [54], indicated significant relationships between the items and indicators of each questionnaire. According to the analyses, the relationship between work organization and accidents among nurses was found to be significant, which can be attributed to the broad content of the questionnaire, covering safety factors and causes of accidents. Various studies, such as Wallace et al. [60], have emphasized the direct and indirect effects of different environments, including management-employee relationships and organizational support, on accidents. Sarsangi et al. [61] also found a significant relationship between mental workload, mental and physical demands of nurses, and occupational accidents. Khosravi et al. [62] highlighted the role of organizational factors in reducing unsafe behaviors and accidents by improving work conditions and individual characteristics. Eskandari et al. [63] reported 11 organizational factors as significant contributors to accidents, according to safety experts in Iranian universities and industries.

The global context of adaptation to environmental challenges such as heat waves also plays a crucial role in workplace safety. Kiarsi et al. highlighted the significance of organizational and individual strategies in adapting to extreme heat conditions, emphasizing how these strategies can mitigate risks associated with environmental stressors [64]. This aligns with the broader findings of the present study, where organizational factors, including work conditions and safety measures, were identified as significant contributors to reducing occupational accidents.

Regarding the relationship between demographic characteristics and accidents, the results showed significant associations between accidents and age, gender, and education level. These results may be attributed to the higher proportion of women (74.5%) and nurses with bachelor’s degrees or higher (91%) in the sample, marital status, family responsibilities, and the greater impact of fatigue on women. Similarly, Shokohyar et al. [65] reported age and gender as significant factors in road accidents.

According to study findings, no significant relationship was found between work organization and fatigue among nurses. This may be explained by the indirect effect of workload—one of the dimensions of work organization—on fatigue, particularly through its impact on sleep quality, as observed in the study by Ghasemi et al. [66] on nurses with similar age and work experience. Another explanation could be the exclusion of the physical effort dimension from the Fatigue Questionnaire due to its low factor loadings, as most nurses experience both physical and mental fatigue from work. Jang HJ and Tirvienė [67, 68] identified job control, rest control, and shift planning as significant facilitators for fatigue recovery, which were not addressed in this study.

Similarly, in line with Abdali et al. [69], no significant relationship was found between fatigue and demographic variables such as gender, marital status, and education level. However, a significant relationship was found between age and fatigue, which may be attributed to differences in the mean age between the two populations. Aging is associated with the gradual deterioration of physiological, circadian, and sleep systems, and evidence suggests a linear relationship between chronological age and fatigue [70].

No significant relationship between fatigue and accidents was found. Occupational accidents occurred infrequently. It can be considered as a reason for difficulty in establishing a direct link with fatigue in the present study. Besides, individual differences, such as age, experience, or personality traits, could moderate the relationship between fatigue and accidents. That is suggested to investigate in future studies.

Limitations

The limitations of the present study include the cross-sectional design, which limits the ability to identify causal relationships between variables. Reliance on self-reported data, which may be subject to biases such as recall bias or social desirability bias. The study population was limited to nurses in hospitals within a specific region. This may prevent the results from being directly generalized to other occupational groups, industries, or workplace settings. Some tools used, such as the fatigue questionnaire, had issues with reliability or psychometric structure, which may have limited the accuracy of measurements. Certain important factors, such as environmental conditions (e.g., seasonal work pressure), individual characteristics (e.g., physical or mental health status), or cultural differences in workplace settings, were not specifically addressed in this study, and they could potentially influence the results.

Conclusion

The results of this study demonstrated that the Persian version of the Work Organization Assessment Questionnaire (WOAQ) has acceptable validity and reliability. It can be used to assess potential risks in hospital and healthcare work environments, particularly in relation to the design and management aspects for healthcare workers, especially nurses in Iran. Additionally, work organization can influence occupational accidents, and under adverse conditions, it may increase the occurrence of accidents. However, through proper management and improvements in work organization, safety and well-being in the workplace can be enhanced.

Regarding the Persian version of the Fatigue Questionnaire, although it showed good composite reliability and validity, the Cronbach's alpha coefficient was below the acceptable threshold, indicating the need for further revision, refinement, and examination in future studies. Moreover, using more diverse populations and various industries would help improve the generalizability of the results.

Data availability

The data generated and analyzed during this study are not publicly available due to privacy concerns of the participants, particularly the sensitive nature of the data related to nurses and hospital management. However, the data are available from the corresponding author upon reasonable request. The authors would like to express their gratitude to all those who assisted in conducting this research, especially the nurses and hospital managers who participated in the study.

Abbreviations

CIS:

Checklist Individual Strength

WOAQ:

Work Organization Assessment Questionnaire

PLS-SEM:

Partial Least Squares Structural Equation Modeling

AVE:

Average Variance Extracted

References

  1. Landsbergis PA. Interventions to reduce job stress and improve work organization and worker health. In: Unhealthy Work. London: Routledge; 2018. p. 193-209.

  2. Carayon P, Smith MJ, Haims MC. Work organization, job stress, and work-related musculoskeletal disorders. Hum Factors. 1999;41(4):644–63.

    Article  CAS  PubMed  Google Scholar 

  3. Stock SR, Nicolakakis N, Vézina N, Vézina M, Gilbert L, Turcot A, et al. Are work organization interventions effective in preventing or reducing work-related musculoskeletal disorders? A systematic review of the literature. Scand J Work Environ Health. 2018;44(2):113–33.

    PubMed  Google Scholar 

  4. Cordery J, Parker SK. Work organization. In: Oxford Handbook of Human Resource Management. Oxford: Oxford University Press; 2007. p. 187-209.

  5. Johnson JV, Lipscomb J. Long working hours, occupational health and the changing nature of work organization. Am J Ind Med. 2006;49(11):921–9.

    Article  PubMed  Google Scholar 

  6. Campaner A, Heywood JS, Jirjahn U. Flexible work organization and employer-provided training: Evidence from German linked employer-employee data. Kyklos. 2022;75(1):3–29.

    Article  Google Scholar 

  7. Sauter SL, Murphy LR. Work organization interventions: State of knowledge and future directions. Sozial-und Präventivmedizin. 2004;49(2):79–86.

    Article  PubMed  Google Scholar 

  8. Alterman T, Luckhaupt SE, Dahlhamer JM, Ward BW, Calvert GM. Prevalence rates of work organization characteristics among workers in the US: Data from the 2010 National Health Interview Survey. Am J Ind Med. 2013;56(6):647–59.

    Article  PubMed  Google Scholar 

  9. Eskandari D, Jafari MJ, Mehrabi Y, Kian MP, Charkhand H, Mirghotbi M. A qualitative study on organizational factors affecting occupational accidents. Iran J Public Health. 2017;46(3):380–9.

    PubMed  PubMed Central  Google Scholar 

  10. Grzywacz JG, Quandt SA, Marín A, Summers P, Lang W, Mills T, et al. Occupational injury and work organization among immigrant Latino residential construction workers. Am J Ind Med. 2012;55(8):698–706.

    Article  PubMed  Google Scholar 

  11. Lin C, Xu Q, Huang Y. Applications of FFTA–HFACS for analyzing human and organization factors in electric misoperation accidents. Appl Sci. 2021;11(19):9008.

    Article  CAS  Google Scholar 

  12. Marchand A, Demers A, Durand P. Does work really cause distress? The contribution of occupational structure and work organization to the experience of psychological distress. Soc Sci Med. 2005;61(1):1–4.

    Article  PubMed  Google Scholar 

  13. Muntaner C, Li Y, Xue X, Thompson T, O’Campo P, Chung H, et al. County-level socioeconomic position, work organization and depression disorder: A repeated measures cross-classified multilevel analysis of low-income nursing home workers. Health Place. 2006;12(4):688–700.

    Article  PubMed  Google Scholar 

  14. Querstret D, O’Brien K, Skene DJ, Maben J. Improving fatigue risk management in healthcare: A scoping review of sleep-related/fatigue-management interventions for nurses and midwives (reprint). Int J Nurs Stud. 2020;112:103745.

    Article  PubMed  Google Scholar 

  15. Bernard B, Sauter SL, Fine LJ, Petersen MR, Hales T. Psychosocial and work organization risk factors for cumulative trauma disorders in the hands and wrists of newspaper employees. Scand J Work Environ Health. 1992;18(2):119–20.

    PubMed  Google Scholar 

  16. Huang GD, Feuerstein M, Kop WJ, Schor K, Arroyo F. Individual and combined impacts of biomechanical and work organization factors in work-related musculoskeletal symptoms. Am J Ind Med. 2003;43(5):495–506.

    Article  PubMed  Google Scholar 

  17. Jang HJ, Kim O, Kim S, Kim MS, Choi JA, Kim B, et al. Factors affecting physical and mental fatigue among female hospital nurses: The Korea Nurses’ Health Study. Healthcare. 2021;9(2):201.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Poursadeqiyan M, Arefi MF, Khaleghi S, Moghaddam AS, Mazloumi E, Raei M, et al. Investigation of the relationship between the safety climate and occupational fatigue among the nurses of educational hospitals in Zabol. J Educ Health Promot. 2020;9:1–8.

    Google Scholar 

  19. Williamson A, Lombardi DA, Folkard S, Stutts J, Courtney TK, Connor JL. The link between fatigue and safety. Accid Anal Prev. 2011;43(2):498–515.

    Article  PubMed  Google Scholar 

  20. Ghasemi F, Zarei H, Babamiri M, Kalatpour O. Fatigue profile among petrochemical firefighters and its relationship with safety behavior: the moderating and mediating roles of perceived safety climate. Int J Occup Saf Ergon. 2022;28(3):1822–8.

    Article  PubMed  Google Scholar 

  21. Sarani A, Mousavi SH, Salahi S, Hasani F, Abdar ZE, Sheikhbardsiri H. Emotional intelligence and self-efficacy among deputy’s administrative staff of Kerman University of Medical Sciences. J Educ Health Promot. 2020;9(1):105.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Hadian M, Jabbari A, Mousavi SH, Sheikhbardsiri H. Medical tourism development: A systematic review of economic aspects. Int J Healthc Manage. 2021;14(2):576–82.

    Article  Google Scholar 

  23. Mirzaei S, Zamanian Z, Hasan ZJ. Effects of work shifts and mental workload on chronic fatigue among female nurses in intensive care units. J Health Sci Surveill Syst. 2015;3(3):113–8.

    Google Scholar 

  24. Bielić T, Zec D. Influence of ship technology and work organization on fatigue. Pomorski Zbornik. 2004;42(1):263–76.

    Google Scholar 

  25. Smith AP, Smith HN. Workload, fatigue and performance in the rail industry. In: International Symposium on Human Mental Workload: Models and Applications. Cham: Springer. 2017;251–63

  26. Arsintescu L, Chachad R, Gregory KB, Mulligan JB, Flynn-Evans EE. The relationship between workload, performance and fatigue in a short-haul airline. Chronobiol Int. 2020;37(9–10):1492–4.

    Article  PubMed  Google Scholar 

  27. Van Dijk FJ, Swaen GM. Fatigue at work. Occup Environ Med. 2003;60(suppl 1):i1-2.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Fang J, Kunaviktikul W, Olson K, Chontawan R, Kaewthummanukul T. Factors influencing fatigue in Chinese nurses. Nurs Health Sci. 2008;10(4):291–9.

    Article  PubMed  Google Scholar 

  29. Han K, Trinkoff AM, Geiger-Brown J. Factors associated with work-related fatigue and recovery in hospital nurses working 12-hour shifts. Workpl Health Safe. 2014;62(10):409–14.

    Article  Google Scholar 

  30. Querstret D, O’Brien K, Skene DJ, Maben J. Improving fatigue risk management in healthcare: A scoping review of sleep-related/fatigue-management interventions for nurses and midwives. Int J Nurs Stud. 2020;112:103745.

    Article  PubMed  Google Scholar 

  31. Barker LM, Nussbaum MA. Fatigue, performance and the work environment: A survey of registered nurses. J Adv Nurs. 2011;67(6):1370–82.

    Article  PubMed  Google Scholar 

  32. Steege LM, Rainbow JG. Fatigue in hospital nurses—‘Supernurse’ culture is a barrier to addressing problems: A qualitative interview study. Int J Nurs Stud. 2017;67:20–8.

    Article  PubMed  Google Scholar 

  33. Lin SC, Tsai KW, Chen MW, Koo M. Association between fatigue and Internet addiction in female hospital nurses. J Adv Nurs. 2013;69(2):374–83.

    Article  PubMed  Google Scholar 

  34. Bakhshi E, Mazloumi A, Hoseini SM. Relationship between mental fatigue and mental workload among nurses. Zahedan J Res Med Sci. 2019;21(1):e83082.

  35. Querstret D, O’Brien K, Skene DJ, Maben J. Improving fatigue risk management in healthcare: A scoping review of sleep-related/fatigue-management interventions for nurses and midwives. Int J Nurs Stud. 2020;112:103745.

    Article  PubMed  Google Scholar 

  36. Bazazan A, Dianat I, Mombeini Z, Aynehchi A, Jafarabadi MA. Fatigue as a mediator of the relationship between quality of life and mental health problems in hospital nurses. Accid Anal Prev. 2019;126:31–6.

    Article  PubMed  Google Scholar 

  37. Swaen GM, Van Amelsvoort LG, Bültmann U, Kant IJ. Fatigue as a risk factor for being injured in an occupational accident: Results from the Maastricht Cohort Study. Occup Environ Med. 2003;60(suppl 1):i88-92.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Dorrian J, Tolley C, Lamond N, van den Heuvel C, Pincombe J, Rogers AE, Drew D. Sleep and errors in a group of Australian hospital nurses at work and during the commute. Appl Ergon. 2008;39(5):605–13.

    Article  PubMed  Google Scholar 

  39. Scott LD, Rogers AE, Hwang WT, Zhang Y. Effects of critical care nurses’ work hours on vigilance and patients’ safety. Am J Crit Care. 2006;15(1):30–7.

    Article  PubMed  Google Scholar 

  40. Santos SV, Macedo FR, Silva LA, Resck ZM, Nogueira DA, Terra FD. Work accidents and self-esteem of nursing professional in hospital settings. Rev Lat Am Enfermagem. 2017;25:e2872.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Ghasemi F, Aghaei H, Askaripoor T, Ghamari F. Analysis of occupational accidents among nurses working in hospitals based on safety climate and safety performance: a Bayesian network analysis. Int J Occup Saf Ergon. 2022;28(1):440–6.

    Article  PubMed  Google Scholar 

  42. Poursadeqiyan M, Arefi MF, Khaleghi S, Moghaddam AS, Mazloumi E, Raei M, Hami M, Khammar A. Investigation of the relationship between the safety climate and occupational fatigue among the nurses of educational hospitals in Zabol. J Educ Health Prom. 2020;9:1–8.

    Google Scholar 

  43. Alahmadi BA, Alharbi MF. Work-related fatigue factors among hospital nurses: An integrative literature review. Nurse Media J Nurs. 2018;8(2):113–33.

    Article  Google Scholar 

  44. Uli R, Modjo R. Factors related to fatigue among nurses in Jambi Mental Hospital: A 2017 Study. KnE Life Sci. 2018;4:190–9.

    Article  Google Scholar 

  45. Bolandianbafghi S, Salimi T, Rassouli M, Faraji R, Sarebanhassanabadi M. Correlation between medication errors with job satisfaction and fatigue of nurses. Electron Phys. 2017;9(8):5142–50.

    Google Scholar 

  46. Seitz MA. Impact of nurse fatigue and nursing handoffs on patient and nurse safety. [Dissertation/Report]. 2016.

  47. Thompson BJ. Fatigue and the female nurse: A narrative review of the current state of research and future directions. Women’s Health Rep. 2021;2(1):53–61.

    Article  Google Scholar 

  48. Ruggiero JS. Correlates of fatigue in critical care nurses. Res Nurs Health. 2003;26(6):434–44.

    Article  PubMed  Google Scholar 

  49. Griffiths A, Cox T, Karanika M, Khan S, Tomás JM. Work design and management in the manufacturing sector: Development and validation of the Work Organisation Assessment Questionnaire. Occup Environ Med. 2006;63(10):669–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Beurskens AJ, Bültmann U, Kant I, Vercoulen JH, Bleijenberg G, Swaen GM. Fatigue among working people: Validity of a questionnaire measure. Occup Environ Med. 2000;57(5):353–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Khandan M, Momenyan S, AtaeiManesh L, Khosravi Z, Eyni Z, Koohpaei A. The relationship between fatigue and job content with musculoskeletal disorders among nurses. Jundishapur J Health Sci. 2017;9(2):e40491.

    Google Scholar 

  52. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18(1):39–50.

    Article  Google Scholar 

  53. Magner N, Welker RB, Campbell TL. Testing a model of cognitive budgetary participation processes in a latent variable structural equations framework. Account Bus Res. 1996;27(1):41–50.

    Article  Google Scholar 

  54. Carayon P, Smith MJ. Work organization and ergonomics. Appl Ergon. 2000;31(6):649–62.

    Article  CAS  PubMed  Google Scholar 

  55. Karimi L, Meyer D. Validity and model-based reliability of the Work Organisation Assessment Questionnaire among nurses. Nurs Outlook. 2015;63(3):318–30.

    Article  PubMed  Google Scholar 

  56. Karimi L, Oakman J. The Work Organisation Assessment Questionnaire: Validation for use with community nurses and paramedics. JBI Evid Implement. 2020;18(2):222–30.

    Google Scholar 

  57. Ergin G, Yildirim Y. A validity and reliability study of the Turkish Checklist Individual Strength (CIS) questionnaire in musculoskeletal physical therapy patients. Physiother Theory Pract. 2012;28(8):624–32.

    Article  PubMed  Google Scholar 

  58. Aratake Y, Tanaka K, Wada K, Watanabe M, Katoh N, Sakata Y, Aizawa Y. Development of Japanese version of the checklist individual strength questionnaire in a working population. J Occup Health. 2007;49(6):453–60.

    Article  PubMed  Google Scholar 

  59. Worm-Smeitink M, Gielissen M, Bloot L, Van Laarhoven HW, Van Engelen BG, Van Riel P, et al. The assessment of fatigue: Psychometric qualities and norms for the Checklist Individual Strength. J Psychosom Res. 2017;98:40–6.

    Article  CAS  PubMed  Google Scholar 

  60. Wallace JC, Popp E, Mondore S. Safety climate as a mediator between foundation climates and occupational accidents: A group-level investigation. J Appl Psychol. 2006;91(3):681–92.

    Article  PubMed  Google Scholar 

  61. Sarsangi V, Salehiniya H, Hannani M, Marzaleh MA, Abadi YS, Honarjoo F, et al. Assessment of workload effect on nursing occupational accidents in hospitals of Kashan. Iran Biomed Res Ther. 2017;4(8):1527–40.

    Article  Google Scholar 

  62. Khosravi Y, Asilian-Mahabadi H, Hajizadeh E, Hassanzadeh-Rangi N, Bastani H, Behzadan AH. Factors influencing unsafe behaviors and accidents on construction sites: A review. Int J Occup Saf Ergon. 2014;20(1):111–25.

    Article  PubMed  Google Scholar 

  63. Eskandari D, Jafari MJ, Mehrabi Y, Kian MP, Charkhand H, Mirghotbi M. A qualitative study on organizational factors affecting occupational accidents. Iran J Public Health. 2017;46(3):380–9.

    PubMed  PubMed Central  Google Scholar 

  64. Kiarsi M, Amiresmaili M, Mahmoodi MR, Farahmandnia H, Nakhaee N, Zareiyan A, Aghababaeian H. Heat waves and adaptation: A global systematic review. J Therm Biol. 2023;16:103588.

    Article  Google Scholar 

  65. Shokohyar S, Taati E, Zolfaghari S. The effect of drivers’ demographic characteristics on road accidents in different seasons using data mining. Promet-Traffic Transport. 2017;29(6):555–67.

    Article  Google Scholar 

  66. Ghasemi F, Samavat P, Soleimani F. The links among workload, sleep quality, and fatigue in nurses: A structural equation modeling approach. Fatigue: Biomed Health Behavior. 2019;7(3):141–52.

    Google Scholar 

  67. Jang HJ, Kim O, Kim S, Kim MS, Choi JA, Kim B, et al. Factors affecting physical and mental fatigue among female hospital nurses: The Korea Nurses’ Health Study. Healthcare. 2021;9(2):201.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Tirvienė G, Spirgienė L, Šimatonienė V. Fatigue among intensive care unit nurses. Nurs Educ Res Pract. 2020;10:4–9.

    Google Scholar 

  69. Abdali N, Nobahar M, Ghorbani R. Evaluation of emotional intelligence, sleep quality, and fatigue among Iranian medical, nursing, and paramedical students: A cross-sectional study. Qatar Med J. 2020;2019(3):15.

    Article  Google Scholar 

  70. Di Milia L, Smolensky MH, Costa G, Howarth HD, Ohayon MM, Philip P. Demographic factors, fatigue, and driving accidents: An examination of the published literature. Accid Anal Prev. 2011;43(2):516–32.

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors would like to express their gratitude to all those who assisted in conducting this research, especially the nurses and hospital managers who participated in the study.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Occupational Health and Safety Engineering, School of Public Health, Qom University of Medical Sciences, Qom, Iran

    Mohammad Khandan & Ali Ebrahimi

  2. Kara Amar Samin, Qom, Iran

    Ali Montazeri

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  1. Mohammad Khandan
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  2. Ali Montazeri
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  3. Ali Ebrahimi
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Contributions

M.KH. conceived the original research idea. A.M, A.E and M.KH contributed to the design and development of the study. A.M and M.KH conducted the data analyses. A.E data collection and drafted the manuscript. All authors read, edited, and approved the final manuscript.

Corresponding author

Correspondence to Ali Ebrahimi.

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Ethics approval and consent to participate

All aspects of the study adhered to the Declaration of Helsinki. A written informed consent was obtained from each participant. At the time of study design and approval, there was no requirement to obtain an ethics approval ID for such studies, according to the regulations of the Ethics Committee of Qom University of Medical Sciences.

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Not applicable.

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The authors declare no competing interests.

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Khandan, M., Montazeri, A. & Ebrahimi, A. Psychometric analysis of work organization and fatigue instruments and their relationship with occupational accidents: a structural equation modeling approach. BMC Health Serv Res 25, 239 (2025). https://doi.org/10.1186/s12913-025-12369-6

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BMC Health Services Research

ISSN: 1472-6963

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