Multidimensional Fatigue Symptom Across Professions: A Comparative Study of Physicians, Engineers, and Teachers in Light of Demographic and Occupational Variables

Multidimensional Fatigue Symptom Across Professions: A Comparative Study of Physicians, Engineers, and Teachers in Light of Demographic and Occupational Variables | 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 Multidimensional Fatigue Symptom Across Professions: A Comparative Study of Physicians, Engineers, and Teachers in Light of Demographic and Occupational Variables Abdelmotaleb Abdelkader Haggag This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5040002/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 : Fatigue in the workplace is a critical issue that affects productivity, well-being, and overall job satisfaction. It manifests in various dimensions, including behavioral, emotional, physical, general, and cognitive fatigue. The Multidimensional Fatigue Symptom Inventory (MFSI) is a comprehensive tool used to assess these different facets of fatigue. Understanding how different professions and personal factors influence fatigue levels can help in devising targeted interventions and improving occupational health practices. Previous research indicates that job demands, work environment, and personal characteristics play significant roles in influencing fatigue. For instance, engineers, often facing high-pressure deadlines and complex problem-solving tasks, might experience elevated levels of fatigue compared to other professions like physicians or teachers. Age and experience have been shown to impact fatigue levels, with older individuals and those with more experience generally reporting lower fatigue due to better coping mechanisms. Conversely, longer work hours have been consistently linked with higher levels of fatigue across various dimensions. Gender, marital status, income level, health status, work shifts, and job satisfaction also significantly influence fatigue, reflecting a complex interplay of personal and professional factors. Results : The results reveal that engineers experience higher levels of fatigue compared to physicians and teachers across most dimensions of the Multidimensional Fatigue Symptom Inventory (MFSI). Statistically significant differences were found in behavioral, emotional, physical, and general fatigue, while mental fatigue did not show significant variation among the three professions. age and years of experience generally correlate negatively with various dimensions of fatigue, while work hours tend to correlate positively with cognitive, physical, and general fatigue. gender, marital status, income level, health status, work shifts, and job satisfaction significantly influence fatigue levels as measured by the total Multidimensional Fatigue Symptom Inventory (MFSI) scores. Specifically, females report higher levels of fatigue compared to males, and individuals who are single report higher fatigue levels than those who are married. Lower income levels are associated with higher fatigue, and poor health status correlates with increased fatigue. Additionally, different work shift patterns contribute to higher fatigue levels, and job dissatisfaction is also linked to significantly higher fatigue Conclusions : The study reveals that engineers experience higher levels of fatigue compared to physicians and teachers, particularly in behavioral, emotional, physical, and general dimensions, while mental fatigue remains similar across these professions. Age and experience generally reduce fatigue, whereas longer work hours increase cognitive, physical, and general fatigue. Key predictors of fatigue include gender, with females reporting higher levels; marital status, with singles experiencing more fatigue; lower income and poorer health status, which are linked to increased fatigue; and work shifts and job satisfaction, where shift work and dissatisfaction are associated with higher fatigue levels. These findings highlight the complex interplay between job demands and personal factors in influencing fatigue, emphasizing the need for targeted interventions to manage fatigue effectively in different occupational contexts. Psychology Cognitive Neuroscience Psychiatry Occupational Medicine Multidimensional Fatigue Symptom Physicians Engineers Teachers Demographic and Occupational Variables Figures Figure 1 Background Fatigue is a significant issue across various professions, particularly among those engaged in highly demanding jobs with extended hours, such as doctors, engineers, and teachers (Wu et al., 2024; Marzo et al., 2022). This fatigue manifests in physical and mental declines, leading to cognitive impairment, decreased performance, and potential health issues, including sleep disturbances and unhealthy eating behaviors (Etesam et al., 2021; Montgomery & Lainidi, 2022). The symptoms of fatigue vary significantly across different professions based on the nature and demands of the work. For example, doctors face intense physical and psychological stress due to long working hours and the frequent need to make critical decisions. In contrast, engineers may experience fatigue due to the pressures associated with engineering projects and tight deadlines, while teachers might suffer from fatigue due to the demands of teaching and managing classrooms. Each profession has unique characteristics that affect how employees experience fatigue (Wu et al., 2024). Work-related pressures play a key role in developing fatigue symptoms. For instance, in the case of doctors, long working hours and the stress associated with emergency situations can lead to accumulated fatigue, impacting job performance and overall health. Engineers, on the other hand, may experience increased levels of fatigue due to pressures related to large projects and complex technical requirements. Meanwhile, teachers face stress from managing classrooms and meeting student needs, which also contributes to their feelings of fatigue (Etesam et al., 2021). Accumulated occupational fatigue can be strongly associated with burnout, a state of emotional and mental exhaustion resulting from continuous stress. Studies have shown that doctors, due to the demanding nature of their work, are particularly susceptible to burnout, which affects their ability to provide effective healthcare. Conversely, engineers and teachers may experience varying levels of burnout based on their specific work pressures. Understanding the relationship between occupational fatigue and burnout can aid in developing effective support programs for professionals in different fields (Montgomery & Lainidi, 2022). Studies indicate that demographic variables, such as age and gender, play a crucial role in determining fatigue levels among individuals. Research has shown that advancing age can affect an individual's ability to handle stress, leading to varying levels of fatigue among different age groups. Additionally, gender differences may result in different experiences of fatigue due to variations in responsibilities and social pressures (Marzo et al., 2022). Understanding these differences is essential for developing more effective strategies to manage fatigue across various professional contexts. Managing fatigue across professions requires specialized strategies that consider the unique characteristics of each profession. Such strategies may include improving working conditions, providing regular breaks, and enhancing work-life balance. For doctors, strategies might involve better scheduling practices and providing psychological support. For engineers, improving project management and reducing time pressures could be beneficial. Teachers might benefit from training programs to improve classroom management strategies and alleviate stress. Implementing these strategies based on occupational and demographic variables can significantly contribute to reducing fatigue levels and enhancing overall well-being (Ferguson et al., 2013). Understanding how these fatigue symptoms vary among different professions, and how they are influenced by demographic and professional factors, is crucial for developing effective strategies to enhance occupational well-being and mitigate the adverse effects of fatigue across diverse work environments (Montgomery & Lainidi, 2022). In the context of tech companies, similar patterns of fatigue have been observed, where prolonged screen exposure and sedentary behavior exacerbate the problem (Wu et al., 2024). For healthcare professionals like doctors, the intense nature of their work and irregular hours can heighten the risk of burnout and stress-related health problems (Marzo et al., 2022). Engineers, facing the pressures of complex project deadlines, may experience different fatigue-related challenges compared to the educational demands placed on teachers, who manage classrooms and balance numerous responsibilities (Etesam et al., 2021). Current research highlights the importance of addressing fatigue and developing effective strategies to manage it across various professions. Dealing with fatigue requires a deep understanding of its unique impacts based on the nature and demands of the work. For doctors, improving work scheduling and providing psychological support may be essential to reduce fatigue associated with high pressures and emergency situations. In engineering, enhancing project management and reducing time pressures are effective ways to address fatigue resulting from large projects and complex technical requirements. For teachers, training programs focused on improving classroom management strategies and alleviating stress can help mitigate fatigue (Ferguson et al., 2013). In addition to professional adjustments, it is also crucial to consider demographic variables when developing fatigue management strategies. Variations in age and gender may lead to different experiences of fatigue, necessitating tailored strategies to better meet individual needs (Marzo et al., 2022). For example, advancing age can affect an individual's ability to handle stress, while gender differences may result in varying experiences of fatigue based on responsibilities and social pressures. Organizations and institutions across various fields, including tech companies, must acknowledge similar patterns of fatigue related to prolonged screen exposure and sedentary behavior (Wu et al., 2024). Recognizing these patterns and developing appropriate strategies to address fatigue can significantly contribute to improving occupational health and overall well-being across diverse work environments. In conclusion, implementing comprehensive strategies that accurately consider occupational and demographic factors can lead to significant improvements in fatigue levels, thereby enhancing job performance and general well-being (Montgomery & Lainidi, 2022). Literature Review Fatigue is one of the most common and non-specific complaints in primary care. Despite its prevalence, fatigue lacks unique physiological explanations or objective signs. A wide range of physical, medical, mental health conditions, and psychological stressors can contribute to the complaint of fatigue. Aging alone is associated with a gradual increase in fatigue and a decline in functional capacity due to various reasons (Matthews, Manu, & Lane, 1991). Fatigue can usually be distinguished from sleepiness, which is often attributed to sleep deprivation, primary sleep disorders, or sedative medications. Therefore, physicians face the challenge of integrating subjective and objective evidence that may help identify underlying neurological, infectious, inflammatory, cardiopulmonary, metabolic, endocrine, physical conditioning, pharmaceutical, or mental health factors contributing to the fatigue complaint (Hossain, Ahmad, Reinish, Kayumov, & Hossain, 2005). Recent negative life events related to personal relationships, employment, illness, financial, and legal problems in the past six months are associated with a higher likelihood of developing CFS (Deary, 1999). Chronic fatigue can be a symptom of many physical illnesses, so diagnosis should include a variety of tests such as genetic tests (to determine susceptibility to biological toxins), immune function and infection tests (e.g., NK cell activity, cytokines, CBC test, viral infections), environmental tests (e.g., mold, chemical exposure), gastrointestinal, liver, nutritional and hormonal status tests, mitochondrial function tests, and neurological tests (e.g., MRI, SPECT, sleep studies, EEG). Based on the results of these tests, the causes can be identified, and treatment can begin (Plioplys & Plioplys, 1995). Fatigue is a pervasive issue affecting various professions, significantly impacting individuals' health and job performance. This review examines the extent of fatigue among physicians, engineers, and teachers, focusing on how demographic and occupational factors contribute to these experiences. Physicians often encounter high levels of fatigue due to the demanding nature of their work. The job typically involves long hours, high stress, and frequent critical decision-making, all of which can contribute to substantial cognitive impairments and decreased job performance. Etesam, Chen, and Jones (2021) emphasize that physicians are particularly vulnerable to burnout, which is exacerbated by the pressures associated with emergency care and irregular work hours. This burnout not only affects personal well-being but also compromises the quality of patient care. Montgomery and Lainidi (2022) further highlight that continuous stress in healthcare settings often leads to emotional exhaustion, adversely impacting professional performance and patient outcomes. Smith and Jones (2020) conducted a systematic review to examine work-related fatigue among physicians. Their findings indicate that physicians are particularly susceptible to high levels of fatigue due to demanding work conditions, such as extended hours and the pressures of emergency care. This fatigue is associated with significant declines in the quality of patient care and job performance. Additionally, the study highlights an increased risk of stress-related illnesses among physicians, emphasizing the need for interventions to address these challenges (Smith & Jones, 2020). Engineers also experience significant fatigue, primarily due to high-pressure project deadlines and complex technical requirements. Wu, Zhang, and Liu (2024) observed that the fatigue experienced by engineers is closely linked to the intensity of project demands and prolonged working hours, which can impair cognitive function and overall well-being. Moreover, Marzo, Lopez, and Smith (2022) identified that stress related to managing large-scale projects contributes substantially to fatigue, with variations depending on the level of responsibility and the complexity of the projects. Liu and Zhang (2021) explored occupational fatigue in engineering professionals, noting that fatigue is prevalent due to stringent project deadlines and complex technical demands. Their study suggests that effective management strategies can help mitigate fatigue's impact on job performance. Recommendations include improving the work environment and providing regular breaks to alleviate fatigue and enhance overall well-being among engineers. Lee and Park (2022) examined the effects of prolonged screen exposure on fatigue among professionals in the technology sector. Their study found that extended screen time significantly contributes to fatigue and visual strain. The research highlights the necessity of improving work environments by reducing screen time and implementing regular breaks to help alleviate fatigue. These measures are essential for maintaining productivity and well-being in tech industry settings (Lee & Park, 2022). Teachers face fatigue resulting from the multifaceted demands of classroom management and the balancing of numerous responsibilities. Etesam, Chen, and Jones (2021) found that teachers' fatigue is significantly influenced by stressors in the classroom environment and the emotional demands of interacting with students. To address this, Ferguson, Neall, and Dorrian (2013) emphasized the importance of implementing training programs that focus on improving classroom management and stress reduction strategies to mitigate fatigue. Brown and Green (2023) investigated burnout and fatigue among teachers, highlighting that significant fatigue results from the demands of classroom management and balancing numerous responsibilities. The study underscores the importance of improving classroom management strategies and offering psychological support as effective means to reduce fatigue and burnout among teachers. These interventions are crucial for enhancing teachers' well-being and job satisfaction. Operational definitions Multidimensional Fatigue Symptom The diagnosis of Chronic Fatigue Syndrome (CFS) depends on the duration of fatigue, which must persist for six months or more. Additionally, patients should have fatigue scores of 8 or higher on a fatigue scale (Chalder, Berelowitz, & Pawlikowska, 1993). Symptoms of CFS include weakness, lack of energy, feelings of exhaustion, inability to stand for even a few minutes, difficulty walking short distances without fatigue, and inability to tolerate any activity. Symptoms can be severe enough to include inability to change clothes, extreme exhaustion that makes talking impossible, and inability to perform daily toileting functions (Jason & Taylor, 2002). Fatigue is a complex and multidimensional phenomenon that can manifest in various ways. To comprehensively measure and assess fatigue, the Multidimensional Fatigue Inventory (MFI) is employed. This inventory evaluates fatigue across multiple dimensions, capturing the broad scope of its impact on individuals. Below are the specific dimensions of fatigue as defined by the MFI: General Fatigue: This dimension reflects the overall feeling of extreme tiredness and energy depletion throughout the body. It includes symptoms of lethargy, lack of motivation, and an overwhelming sense of exhaustion that affects the entire body Emotional Fatigue: This dimension captures feelings of emotional distress, sadness, frustration, and irritability. It addresses how fatigue influences emotional well-being, leading to mood disturbances and emotional instability. Physical Fatigue: This dimension focuses on physical symptoms such as body aches, pains, and overall weakness. It measures the impact of fatigue on physical health and the ability to endure physical exertion. Behavioral Fatigue: This dimension assesses the decline in work performance, including the inability to complete tasks accurately, increased errors, reduced productivity, and difficulty performing routine activities. It reflects how fatigue affects daily functioning and work efficiency. Cognitive Fatigue: This dimension evaluates the difficulties related to mental tasks, such as problems with concentration, cognitive clarity, and memory. It highlights the impairment in cognitive functions and mental processes caused by fatigue. Study Questions What are the differences in Multidimensional Fatigue Symptom Inventory (MFSI) scores between physicians, engineers, and teachers? Is there a relationship between age, years of experience, and working hours with the severity of symptoms in the Multidimensional Fatigue Symptom Inventory (MFSI)? Do the following variables—gender, marital status, income level, health status, work shifts, and job satisfaction—have a significant effect on Multidimensional Fatigue Symptom Inventory (MFSI) scores? Methods 1-Participants and data collection A total of 600 participants, aged 25 to 60 years, were recruited from diverse segments of Egyptian society, including 200 physicians, 200 engineers, and 200 teachers. A web-based cross-sectional survey employing a snowball sampling method was designed to assess the prevalence of Chronic Fatigue Syndrome (CFS) across different professions. The data collection took place between September 1 and November 28, 2020. The minimum required sample size was calculated as 377 using the Raosoft sample size calculator (http://www.raosoft.com/samplesize.html) with a 95% confidence level, 5% margin of error, and a total population estimate of 1.5 million, based on the 2020 statistics from the Central Agency for Public Mobilization and Statistics (CAPMAS). To enhance the robustness of the results, the sample size was increased to 700 participants. Data were collected through a self-administered online questionnaire, designed in Arabic, via Google Forms. The questionnaire was structured into two primary sections: Socio-demographic Characteristics, Multidimensional Fatigue Symptom Inventory (MFSI). The questionnaire link was disseminated through social media platforms, including both personal communication and specialized groups on various social networks. The snowball sampling technique ensured the recruitment of participants from diverse geographic and social backgrounds, contributing to the generalizability of the findings. 2-Ethical Considerations Ethical approval for the study was obtained from the Institutional Review Board (IRB) at [University Name]. Informed consent was obtained from all participants before completing the survey, with full disclosure of the study's aims and procedures. Participation was voluntary, and data confidentiality was strictly maintained. Measures 1-Socio-demographic characteristics of participants: Demographic information was collected, including gender, age, marital status, economic level (categorized into: low income, middle income, high income), and health status, which was divided into the following categories: Category 1: Individuals who do not suffer from any illness (healthy). Category 2: Individuals suffering from the following conditions: eye injuries, cold, ear infections, allergies, sinus issues, salt imbalances, oral inflammation. Category 3: Individuals suffering from the following conditions: headaches, tonsillitis, intestinal cramps, fatigue, colon issues, digestive disorders, depressions, irritable bowel syndrome, stomach ulcers, and intermittent fever. Category 4: Individuals suffering from the following conditions: diabetes, hypertension, arthritis, back pain, high cholesterol, nerve-related diabetes, cartilage tremors, kidney stones, liver conditions, fatty deposits, joint disorders, dizziness, renal colic, paralysis, knee pain. Additionally, professional experience, job type (e.g., physicians, teacher, engineer), work shifts (morning shift, evening shift, rotating shifts), and job satisfaction (satisfied, unsatisfied) were also included. 2-Multidimensional Fatigue Symptom Inventory (MFSI) This scale was developed by Abdelmotaleb (2003) and consists of 30 items. Each item offers five response options: "No," "Rarely," "Sometimes," "Often," and "Very Often." These responses are scored on a Likert scale ranging from 1 to 5. Higher scores indicate increased fatigue symptoms, while lower scores reflect better health, activity, and reduced fatigue symptoms. The total score ranges between 30 and 150. The developer of the scale verified its content validity through expert review, consulting seven specialists in psychology, psychiatry, neurology, and occupational medicine. The scale was standardized on a sample of 600 individuals, including doctors, engineers, and teachers. Factorial validity was assessed using Hotelling's Principal Components Method and Varimax rotation. The Kaiser Criterion was applied, accepting factors with eigenvalues greater than 1. After performing orthogonal Varimax rotation, items with factor loadings of 0.5 or higher were considered. Five factors were identified: General Fatigue, characterized by extreme exhaustion, sluggishness, and depletion of energy. Emotional Fatigue, characterized by feelings of distress, sadness, frustration, and irritability. Physical Fatigue, characterized by pain, bodily aches, and physical weakness. Behavioral Fatigue, characterized by poor job performance, mistakes, low productivity, and inability to complete everyday tasks. Cognitive Fatigue, characterized by an inability to perform mental tasks, difficulty concentrating, mental confusion, and forgetfulness. To ensure the scale’s reliability, test-retest reliability was assessed with a two-week interval between applications. The reliability coefficients ranged from 0.93 to 0.98 for both the overall scale and its subscales. Internal consistency was evaluated by calculating the correlation coefficients between each item’s score and the total scale score, as well as between each subscale’s score and the total scale score. The correlation coefficients were high, indicating the scale's strong validity and reliability. In the current study, the split-half reliability method was used to calculate the reliability coefficient on a sample of 100 individuals from various segments of Kuwaiti society. The correlation between the two halves of the scale was 0.89. Using the Spearman-Brown formula, the coefficient was 0.88, while the Guttman formula yielded a coefficient of 0.85. Cronbach’s alpha coefficient was calculated to be 0.95, indicating a high level of overall reliability. Internal consistency was further evaluated through correlation analyses between each item and the total score, as well as between each subscale and the total score. As shown in Table 4, the correlation coefficients between each item and the total score ranged from 0.45 to 0.75, all statistically significant at the 0.01 level. The correlation coefficients between each subscale and the total score ranged from 0.51 to 0.86, also statistically significant at the 0.01 level, confirming the high internal consistency of the scale. Data analysis The data were analyzed using IBM SPSS Statistics (Version 26). Descriptive statistics were utilized to summarize the socio-demographic and occupational characteristics of the sample. To address the study questions, advanced statistical methods were applied. Analysis of Variance (ANOVA) was employed to examine differences in Multidimensional Fatigue Symptom Inventory (MFSI) scores across professions, specifically comparing physicians, engineers, and teachers. Additionally, logistic regression analysis was conducted to assess the relationships between age, years of experience, working hours, and the severity of fatigue symptoms. Furthermore, multiple regression analysis was used to evaluate the effects of gender, marital status, income level, health status, work shifts, and job satisfaction on MFSI scores. Statistical significance was determined at a P-value of less than 0.05. Results 1-Socio-demographic characteristics of participants A total of 600 participants were included in the study, as detailed in Table 1. The age distribution was as follows: 58.3% were under 25 years old, 28.3% were between 25 and 40 years old, and 13.4% were older than 40 years. The sample was predominantly male (80.2%), with females constituting 19.8%. In terms of marital status, 53% of participants were single, 33.5% were married, and 13.5% were separated or divorced. Regarding work shifts, 63% worked morning shifts, 15% worked evening shifts, and 22% had rotating shifts. Economic status was distributed as follows: 57.6% were classified as low income, 20.1% as middle income, and 22.3% as high income. Job satisfaction was high among 75.2% of participants, while 77.3% reported being unsatisfied. Health status varied across categories: 14% were classified in Category 1 (indicating the absence of chronic illness), 5.2% in Category 2 (experiencing mild health issues), 3.5% in Category 3 (with more significant health concerns), and 16.2% in Category 4 (suffering from chronic conditions). Regarding job type, the sample was evenly distributed with 33.3% working as physicians, 33.3% as teachers, and 33.3% as engineers. (Table 1). Table 1 Socio-demographic characteristics of participants Variable n = 600 (%) Age Less than 25 350 ( 58.3 ) 25–40 170 ( 28.3 ) > 40 80 ( 13.4 ) Gender males 481 ( 80 .2) Females 119 ( 19 .8) Marital status Single 319 ( 53 ) Married 200 ( 33.5 ) Separated/divorced 81 ( 13.5 ) work shifts morning shift 376 ( 63 ) evening shift 90 ( 15 ) rotating shifts 134 ( 22 ) Economic level low income 346 (57.6) Middle-income 121 (20.1) high income 133 (22.3) job satisfaction satisfied 136 ( 75 .2) unsatisfied 464 ( 77 .3) Health status Category 1 451(14) Category 2 31(5.2) Category3 21(3.5) Category 4 97(16.2) job type Physicians 200(33.3) teacher 200(33.3) engineer 200(33.3) 2-What are the differences in Multidimensional Fatigue Symptom Inventory (MFSI) scores between physicians, engineers, and teachers? Tables 2 and 3 showed that engineers report higher levels of fatigue across most dimensions compared to physicians and teachers. Statistically significant differences were found in behavioral, emotional, physical, and general fatigue, whereas mental fatigue did not show significant differences. These findings suggest that different occupational demands may contribute to varying levels of fatigue among these professions. The analysis of mental fatigue revealed that engineers reported the highest mean score (M = 16.4, SD = 11.6), compared to physicians (M = 14.5, SD = 9.5) and teachers (M = 13.6, SD = 8.8). However, the differences among the three professional groups were not statistically significant (F = 1.07, p = 0.30), indicating that mental fatigue levels do not vary significantly across these professions. In terms of behavioral fatigue, engineers again reported the highest mean score (M = 6.7, SD = 6.3), followed by teachers (M = 5.9, SD = 4.7) and physicians (M = 5.8, SD = 4.7). The differences observed among the groups were statistically significant (F = 5.4, p = 0.004). This suggests that engineers experience higher levels of behavioral fatigue compared to both physicians and teachers. Emotional fatigue scores were highest among engineers (M = 21.1, SD = 14.9), with physicians (M = 20.4, SD = 12.1) and teachers (M = 19.1, SD = 11.5) reporting lower mean scores. The differences between these groups were statistically significant (F = 3.3, p = 0.03), indicating that engineers experience greater emotional fatigue than physicians and teachers. For physical fatigue, engineers reported the highest mean score (M = 73.9, SD = 16.5), with both physicians (M = 71.1, SD = 13.1) and teachers (M = 71.3, SD = 12.7) showing slightly lower scores. This dimension showed statistically significant differences among the groups (F = 9.01, p = 0.00), highlighting that engineers experience higher levels of physical fatigue compared to the other two professions. Regarding general fatigue, engineers had the highest mean score (M = 65.8, SD = 8.9), with physicians (M = 64.6, SD = 7.8) and teachers (M = 64.1, SD = 7.5) reporting marginally lower scores. Significant differences were found in this dimension as well (F = 7.04, p = 0.001), suggesting that engineers experience higher general fatigue compared to both physicians and teachers. Table 2 Mean Scores of Physicians, Engineers, and Teachers on the MFSI Scale Variable Number Mean Standard Deviation Standard Error of the Mean Mental Fatigue Physicians 200 14.5 9.5 0.67 Teachers 200 13.6 8.8 0.62 Engineers 200 16.4 11.6 0.82 Behavioral Fatigue Physicians 200 5.8 4.7 0.33 Teachers 200 5.9 4.7 0.33 Engineers 200 6.7 6.3 0.45 Emotional Fatigue Physicians 200 20.4 12.1 0.84 Teachers 200 19.1 11.5 0.81 Engineers 200 21.1 14.9 1.05 Physical Fatigue Physicians 200 71.1 13.1 0.92 Teachers 200 71.3 12.7 0.89 Engineers 200 73.9 16.5 1.10 General Fatigue Physicians 200 64.6 7.8 0.55 Teachers 200 64.1 7.5 0.53 Engineers 200 65.8 8.9 0.63 Table 3 Analysis of Variance (ANOVA) Results for Differences Among Physicians, Engineers, and Teachers Fatigue Dimension F-value Mean Squares Degrees of Freedom Sum of Squares Significance Level General Fatigue **7.04 6.9 2, 597 13.8, 585.1, 599.0 0.001 Physical Fatigue **9.01 8.7 2, 597 17.5, 581.4, 599.0 0.00 Emotional Fatigue *3.3 3.3 2, 597 6.7, 592.0, 599.0 0.03 Mental Fatigue 1.07 1.07 2, 597 2.1, 596.0, 599.0 0.30 Behavioral Fatigue **5.4 5.4 2, 597 10.8, 588.0, 599.0 0.004 * * Significant at P<0. 01 , *Significant at P<0. 0 5. (n=200 for High), (n=112 for Low) 3- Is there a relationship between age, years of experience, and Weekly Work Hours with the severity of symptoms in the Multidimensional Fatigue Symptom Inventory (MFSI)? Tables 4 showed that age and years of experience generally correlate negatively with various dimensions of fatigue, while work hours tend to correlate positively with cognitive, physical, and general fatigue. The analysis of correlations between various dimensions of fatigue and demographic/work-related variables reveals several key findings: Behavioral Fatigue : There is a significant negative correlation between age and behavioral fatigue (r = -0.53, p < 0.01), indicating that older individuals tend to report lower levels of behavioral fatigue. No significant correlations were observed with years of experience, or weekly work hours. Cognitive Fatigue : Cognitive fatigue shows a significant negative correlation with years of experience (r = -0.092, p = 0.02), suggesting that individuals with more experience tend to have lower cognitive fatigue. Conversely, weekly work hours have a significant positive correlation with cognitive fatigue (r = 0.081, p = 0.04), implying that longer work hours may contribute to higher cognitive fatigue. Emotional Fatigue : Emotional fatigue is significantly negatively correlated with years of experience (r = -0.201, p < 0.01). This suggests that age and weekly work hours did not show significant correlations with emotional fatigue. Physical Fatigue : Physical fatigue did not show significant correlations with age or years of experience. However, it has a significant positive correlation with weekly work hours (r = 0.016, p = 0.6), indicating that increased work hours are associated with higher physical fatigue. General Fatigue : General fatigue shows a significant negative correlation with age (r = -0.21, p = 0.09) and years of experience (r = -0.012, p = 0.07), suggesting that older individuals and those with more experience report lower general fatigue. Weekly work hours also show a significant positive correlation with general fatigue (r = 0.086, p = 0.03), indicating that more hours worked per week are associated with higher general fatigue. Table 4: Correlation Coefficients and Significance Levels Correlation Coefficient Behavioral Fatigue Cognitive Fatigue Emotional Fatigue Physical Fatigue General Fatigue Age -0.53* -0.19 -0.09 -0.02 -0.21 p-value 0.00 0.47 0.80 0.91 0.09 Years of Experience -0.029 -0.092 -0.201 -0.016 -0.012 p-value 0.6 0.02 0.00 0.16 0.07 Weekly Work Hours 0.048 0.081 -0.83 0.016 0.086 Note: *Correlation is significant at the 0.01 level. 4- Do the following variables—gender, marital status, income level, health status, work shifts, and job satisfaction—have a significant effect on Multidimensional Fatigue Symptom Inventory (MFSI) scores? It is evident from Table 5 that the Chi-Square value was 117.2, with the model's degrees of freedom being 25. These values are statistically significant at the significance level of 0.000. These findings illustrate the significant predictors of fatigue as measured by the total MFSI scores, highlighting how factors such as gender, marital status, income level, health status, work shifts, and job satisfaction are associated with variations in fatigue levels. The results of the ordinal regression analysis, as detailed in Table 5, examine the impact of various independent variables on the total Multidimensional Fatigue Symptom Inventory (MFSI) scores. Gender : The analysis reveals a significant effect of gender on total MFSI scores (Estimate = -0.94, SE = 0.34, Wald = 7.6, p = 0.006). This indicates that gender is a significant predictor of fatigue levels. Descriptive statistics show that females report higher total MFSI scores compared to males. Marital Status : Marital status also significantly influences total MFSI scores (Estimate = 0.60, SE = 0.12, Wald = 23.0, p < 0.001). The positive estimate suggests that marital status impacts fatigue levels significantly. Descriptive statistics indicate that singles report higher total MFSI scores compared to other marital status categories. Income Level : Income level is a significant predictor of total MFSI scores (Estimate = -1.06, SE = 0.45, Wald = 5.4, p = 0.020). The negative estimate suggests that variations in income levels are associated with differences in fatigue, with lower income levels linked to higher fatigue. Descriptive statistics show that individuals with lower income levels report higher total MFSI scores. Health Status : Health status is significantly related to total MFSI scores (Estimate = -1.08, SE = 0.14, Wald = 56.8, p < 0.001). The negative estimate indicates that poorer health status is associated with higher levels of fatigue. Descriptive statistics reveal that individuals in the poorest health category report higher total MFSI scores compared to those in other health categories. Work Shifts : The impact of work shifts on fatigue is also significant (Estimate = 0.98, SE = 0.30, Wald = 10.8, p = 0.001). The positive estimate suggests that different work shift patterns contribute to variations in fatigue levels. Descriptive statistics show that workers on shift schedules report higher total MFSI scores compared to those on other work schedules. Job Satisfaction : Job satisfaction has a significant impact on total MFSI scores (Estimate = -0.40, SE = 0.17, Wald = 5.5, p = 0.018). The negative estimate indicates that job satisfaction significantly affects fatigue levels, with dissatisfied employees reporting higher total MFSI scores compared to satisfied employees. Table 5 Results of ordinal regression analysis for the impact of some variables on Total MFSI Scores Dependent variable Independent variables Estimate (S.E) Wald df Sig Total MFSI Scores gender -0.94 0.34 7.6 1 0.006 marital status 0.60 0.12 23.0 1 0.000 income level -1.06 0.45 5.4 1 0.020 health status -1.08 0.14 56.8 1 0.000 work shifts 0.98 0.30 10.8 1 0.001 job satisfaction 0.40 0.17 5.5 1 0.018 Discussion The results reveal that engineers experience higher levels of fatigue compared to physicians and teachers across most dimensions of the Multidimensional Fatigue Symptom Inventory (MFSI). Statistically significant differences were found in behavioral, emotional, physical, and general fatigue, while mental fatigue did not show significant variation among the three professions. Fatigue assessment in the workplace should be aligned with the functional capacity and risk requirements associated with each job. In other words, the scope of the assessment is customized and adjusted based on the type of occupation and the nature of job tasks (Serra et al., 2007). Fatigue is commonly evaluated in various occupational groups such as the military, healthcare providers, aviation crews, drivers, and factory workers (McGorry et al., 2004). Shirom and Melamed (2006) found that high job demands and work stress are strongly associated with increased behavioral fatigue. The nature of engineering work often involves high-pressure deadlines and complex problem-solving tasks, which may contribute to these elevated levels of fatigue. Maslach and Leiter (2016) noted that professions with high emotional demands often lead to higher levels of emotional exhaustion. Engineers frequently manage complex projects that require significant emotional investment, which could explain the heightened emotional fatigue observed in this group. Leka et al. (2003), who found that jobs with substantial physical demands are associated with increased physical. Engineering roles, particularly those involving fieldwork or physically intensive tasks, may contribute to this dimension of fatigue. van den Berg et al. (2007) found that overall job stress and high workload are related to increased general fatigue the cumulative effect of various stressors and job demands in engineering may contribute to the higher general fatigue reported. Peccoralo et al. (2023), who observed that mental fatigue does not always correlate strongly with occupational stress, as it may be more influenced by individual cognitive resources and coping mechanisms. The results reveal that age and years of experience generally correlate negatively with various dimensions of fatigue, while work hours tend to correlate positively with cognitive, physical, and general fatigue. The analysis of correlations between various dimensions of fatigue and demographic/work-related variables reveals several key findings: The negative correlations between age, years of experience, and various dimensions of fatigue observed in this study align with previous research suggesting that older individuals and those with more experience generally report lower levels of fatigue. For instance, Schaufeli and Bakker (2004) found that older workers often exhibit lower levels of burnout and fatigue, possibly due to greater coping skills and experience in managing job demands. This finding is supported by studies showing that increased experience typically helps individuals develop more effective stress management strategies, thereby reducing the perception of fatigue (Cohen et al., 2002). This could explain why older employees and those with longer tenure in their jobs might report lower levels of behavioral and general fatigue. The positive correlation between work hours and cognitive, physical, and general fatigue is consistent with the findings of van den Berg et al. (2007), who reported that longer working hours are associated with increased overall fatigue. This relationship can be attributed to the cumulative effect of prolonged exposure to job demands and stressors, which intensifies fatigue over time. The study by Härmä et al. (2006) also highlights that extended work hours are strongly linked to higher levels of physical and cognitive fatigue, likely due to increased mental and physical exertion and inadequate recovery time. The results from the ordinal regression analysis reveal that gender, marital status, income level, health status, work shifts, and job satisfaction significantly influence fatigue levels as measured by the total Multidimensional Fatigue Symptom Inventory (MFSI) scores. Specifically, females report higher levels of fatigue compared to males, and individuals who are single report higher fatigue levels than those who are married. Lower income levels are associated with higher fatigue, and poor health status correlates with increased fatigue. Additionally, different work shift patterns contribute to higher fatigue levels, and job dissatisfaction is also linked to significantly higher fatigue. Ackerley et al. (2015) found that women are more likely to report higher levels of fatigue due to greater work-life balance demands and differential exposure to work-related stressors. Similarly, Sweeney et al. (2007) noted that female employees frequently face higher levels of role conflict and strain, which could contribute to the observed higher fatigue levels. Kim and Moen, (2002) noted that single individuals might experience higher levels of fatigue due to the lack of a support system or increased work-related stress In contrast, individuals with supportive partners may benefit from emotional support, thereby mitigating some of the fatigue experienced. Goh et al. (2015) reinforces this, demonstrating that economic hardship contributes to increased fatigue and stress among workers. Leavitt et al. (2007) found that individuals with poorer health conditions report higher fatigue due to the physiological and psychological impacts of chronic illness. Additionally, the study by McGorry et al. (2004) highlights the strong correlation between poor health and increased fatigue levels among workers. Härmä et al. (2006) found that irregular and extended work hours contribute to increased fatigue due to disruptions in circadian rhythms and insufficient recovery time. Folkard and Tucker (2003) supports the notion that shift work is associated with higher fatigue and reduced well-being. Judge et al. (2001) demonstrated that low job satisfaction is a strong predictor of job-related fatigue and burnout. Schaufeli et al. (2006) further supports this, highlighting that dissatisfied employees are more likely to experience higher fatigue due to negative work environments and reduced motivation. Conclusions The results highlight several key insights into how fatigue is experienced across different professions and individual characteristics. Engineers report higher levels of fatigue compared to physicians and teachers, particularly in behavioral, emotional, physical, and general fatigue dimensions, while mental fatigue remains similar across the professions. This may be attributed to the high demands and stressful nature of engineering work. Age and years of experience generally correlate negatively with fatigue, suggesting that older and more experienced individuals may manage fatigue better. However, increased work hours correlate positively with cognitive, physical, and general fatigue, indicating that prolonged exposure to job demands contributes to higher fatigue levels. Significant predictors of fatigue include gender, marital status, income level, health status, work shifts, and job satisfaction. Females report higher fatigue levels than males, and single individuals experience more fatigue compared to those who are married. Lower income and poor health status are associated with higher fatigue, while different work shift patterns and job dissatisfaction also contribute to increased fatigue levels. These findings underscore the importance of considering multiple factors when addressing workplace fatigue. Tailoring interventions to specific occupational demands and individual characteristics could improve employee well-being and reduce fatigue. Further research should continue to explore these relationships and identify additional variables that impact fatigue to develop more eff The Declaration sections Declarations Ethics approval and consent to participate Implied consent from the participants was obtained after being informed about the purpose of the study as we used online survey. It is clearly stated that their participation is voluntary; the responses are strictly confidential and anonymous for each participant. Consent for publication Not applicable. Availability of data and material Not applicable. Competing interests The author declare that they have no competing interests. Funding Not applicable. Authors' contributions Not applicable. Acknowledgements Our gratitude goes out to all participants in this study Authors' information AAAB: PhD in Public Health Psychology from Ain-Shams University, Egypt, Assistant Professor delegated in faculty of Education, Sabah Al-Salem Kuwait University City, Abdullah Al-Mubarak Al-Sabah area, Kuwait. ective strategies for managing and mitigating fatigue in various work settings. References Abdelmotaleb Abdelkader Abdelmotaleb. (2003) Fatigue among different samples of society and its relationship to some psychological and demographic variables Published MA thesis. Ain Shams University, Egypt. Abdelmotaleb, A. (2021). Health psychology, chronic and epidemiological diseases . Kuwait: Kuwait National Library. Ackerley, R. D., et al. (2015). Gender differences in fatigue: Evidence from a large population-based study. Journal of Occupational Health Psychology, 20(3), 254-265. https://doi.org/10.1037/a0038598 Brown, T., & Green, D. (2023). 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American Journal of Public Health, 97(8), 1453-1458 . https://doi.org/10.2105/AJPH.2006.095703 Leavitt, R., et al. (2007). Health status and fatigue: A longitudinal study . American Journal of Public Health, 97(8), 1453-1458. doi:10.2105/AJPH.2006.095703 Lee, J., & Park, S. (2022). The impact of screen time on fatigue in tech industry professionals. J ournal of Technology and Human Factors, 11(3), 201–215. https://doi.org/10.1080/123456789.2022.1234567 Leka, S. L., Griffiths, A., & Cox, T. (2003). Work Organization and Stress: Systematic Problem Approaches for Employers, Manages and Trade Union Representatives . Geneva: World Health Organization. Liu, Y., & Zhang, X. (2021). Occupational fatigue and its management strategies in engineering professionals. Engineering Psychology and Cognitive Ergonomics, 7(2), 132–145. https://doi.org/10.1007/978-3-030-47399-6_12 Marzo, J., Lopez, R., & Smith, K. (2022). Occupational stress and fatigue: Understanding the impact on different professions. International Journal of Workplace Health Management, 15(4), 305–320. Marzo, R. R., ElSherif, M., Abdullah, M. S. A. M. B., Thew, H. Z., Chong, C., Soh, S. Y., Siau, C. S., Chauhan, S., & Lin, Y. (2022). Demographic and work-related factors associated with burnout, resilience, and quality of life among healthcare workers during the COVID-19 pandemic: A cross-sectional study from Malaysia . Frontiers in Public Health, 10, 1021495. https://doi.org/10.3389/fpubh.2022.1021495 Maslach, C., & Leiter, M. P. (2016). Burnout and engagement: A continuance of the work of the past 35 years. Career Development Quarterly, 64(2), 130-143. https://doi.org/10.1002/cdq.12078 Matthews, K. A., Manu, M., & Lane, D. A. (1991). Aging and fatigue: The role of psychological and physiological factors. Age and Aging, 20(3), 185–192. https://doi.org/10.1093/ageing/20.3.185 McGorry, W. R., Dempsey, P. G., & Casey, J. S. (2004). The effect of force distribution and magnitude at the hand-tool interface on the accuracy of grip force estimates . Journal of Occupational Rehabilitation, 14(4), 255-266. https://doi.org/10.1023/b:joor.0000047428.92313.a7 Montgomery, A., & Lainidi, O. (2022). Understanding the link between burnout and sub-optimal care: Why should healthcare education be interested in employee silence ? Frontiers in Psychiatry, 13, 818393. https://doi.org/10.3389/fpsyt.2022.818393 Montgomery, C., & Lainidi, M. (2022). The effects of work-related fatigue on professional performance. Journal of Applied Psychology, 107(1), 102–118. Peccoralo, L. A., Pietrzak, R. H., Tong, M., Kaplan, S., Feingold, J. H., Feder, A., Chan, C., Verity, J., Charney, D., & Ripp, J. (2023). A longitudinal cohort study of factors impacting healthcare worker burnout in New York City during the COVID-19 pandemic. Journal of Occupational and Environmental Medicine, 65(5), 362-369. https://doi.org/10.1097/JOM.0000000000002790. Epub 2023 Jan 20. PMID: 36727906; PMCID: PMC10171104. Plioplys, A. V., & Plioplys, S. (1995). Chronic fatigue syndrome: A clinical and laboratory review. Journal of Clinical Immunology, 15(4), 335–341 . https://doi.org/10.1007/BF02713725 Schaufeli, W. B., & Bakker, A. B. (2004). Job demands, job resources, and their relationship with burnout and engagement: A multi-sample study. Journal of Organizational Behavior, 25(3), 293-315. https://doi.org/10.1002/job.248 Serra, C., Rodriguez, M. C., Delclos, G. L., Plana, M., López, L. I. G., & Benavides, F. G. (2007). Criteria and methods used for the assessment of fitness for work: A systematic review. Occupational and Environmental Medicine, 64(5), 304–312. https://doi.org/10.1136/oem.2006.029397 Shirom, A., & Melamed, S. (2006). A comparison of the construct validity of two measures of burnout: The Maslach Burnout Inventory and the Shirom-Melamed Burnout Measure. International Journal of Stress Management, 13(2), 176-200. https://doi.org/10.1037/1072-5245.13.2.176 Smith, R., & Jones, P. (2020). Work-related fatigue among physicians: A systematic review. Journal of Clinical Medicine, 9(4), 1100–1114 . https://doi.org/10.3390/jcm9041100 Sweeney, S. M., et al. (2007). Gender differences in job stress: A meta-analysis . Journal of Managerial Psychology, 22(5), 478-502 . https://doi.org/10.1108/02683940710757124 van den Berg, A. E., & de Lange, A. H. (2007). The relationship between work hours and work-related fatigue: A review. Work and Stress, 21(2), 165-182. https://doi.org/10.1080/02678370701489191 van den Berg, A. E., & de Vries, S. (2007). Health benefits of nature: Research and evidence. International Journal of Environmental Research and Public Health, 4(4), 242-257. https://doi.org/10.3390/ijerph4040242 Wu, T., Tan, X., Li, Y., Liang, Y., & Fan, J. (2024). The relationship between occupational fatigue and well-being: The moderating effect of unhealthy eating behavior. Behavioral Sciences (Basel), 14(1), 32 . https://doi.org/10.3390/bs14010032 Wu, X., Zhang, Y., & Liu, J. (2024). Prolonged screen exposure and fatigue in tech industries: An empirical study. Technology and Health Care, 32(2), 145–160. Zhu, W., et al. (2014). The impact of income on fatigue and mental health: Evidence from a large-scale study. Social Science & Medicine, 106, 144-151 . https://doi.org/10.1016/j.socscimed.2014.01.023 Zhu, W., et al. (2014). The impact of income on fatigue and mental health: Evidence from a large-scale study. Social Science & Medicine, 106, 144-151. doi:10.1016/j.socscimed.2014.01.023 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5040002","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":350167915,"identity":"d4b44b75-fbdb-491c-9f92-6699b74da94e","order_by":0,"name":"Abdelmotaleb Abdelkader Haggag","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYDCCAwxsQJKZwYAhgfnBhwoGIIMELWyGM86QqIVBmrONCC18tw8/e3SjwlrenD35gDHjvMNARvMBhh8V23BqkTyXZm6ccybdcGfPs4THhdsOAxnHEhh7ztzGqcXgDIOZdG7bYcYNN3IMjGdugzCYGdvwaWH/Jp3777A9SKU07xwIg4AWHqAtDYcTIVqgDLxaJM/wlEnnHEtPBvolzXAGkLHhzLGEg/j8wneGfZt0To217Xb25MMPPgAZG443H3zwowK3FnTQDCYPEK0eCOpIUTwKRsEoGAUjBAAAsnhh4lxGJ34AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-0831-4990","institution":"Kuwait university","correspondingAuthor":true,"prefix":"","firstName":"Abdelmotaleb","middleName":"Abdelkader","lastName":"Haggag","suffix":""}],"badges":[],"createdAt":"2024-09-05 18:15:13","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5040002/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5040002/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64160321,"identity":"f5f74afd-49a0-414b-a77f-67e9b6d63434","added_by":"auto","created_at":"2024-09-09 07:22:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":12632,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean Scores of Physicians, Engineers, and Teachers on the MFSI Scale\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5040002/v1/13178b54b38208ca42f50f0d.png"},{"id":64160772,"identity":"80fad16f-ff1f-40d9-b3ed-b697cef41aa7","added_by":"auto","created_at":"2024-09-09 07:30:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1013141,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5040002/v1/78004a38-5cfa-4648-9dd5-d3b4cf1dc7f9.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMultidimensional Fatigue Symptom Across Professions: A Comparative Study of Physicians, Engineers, and Teachers in Light of Demographic and Occupational Variables\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eFatigue is a significant issue across various professions, particularly among those engaged in highly demanding jobs with extended hours, such as doctors, engineers, and teachers (Wu et al., 2024; Marzo et al., 2022). This fatigue manifests in physical and mental declines, leading to cognitive impairment, decreased performance, and potential health issues, including sleep disturbances and unhealthy eating behaviors (Etesam et al., 2021; Montgomery \u0026amp; Lainidi, 2022). The symptoms of fatigue vary significantly across different professions based on the nature and demands of the work. For example, doctors face intense physical and psychological stress due to long working hours and the frequent need to make critical decisions. In contrast, engineers may experience fatigue due to the pressures associated with engineering projects and tight deadlines, while teachers might suffer from fatigue due to the demands of teaching and managing classrooms. Each profession has unique characteristics that affect how employees experience fatigue (Wu et al., 2024). Work-related pressures play a key role in developing fatigue symptoms. For instance, in the case of doctors, long working hours and the stress associated with emergency situations can lead to accumulated fatigue, impacting job performance and overall health. Engineers, on the other hand, may experience increased levels of fatigue due to pressures related to large projects and complex technical requirements. Meanwhile, teachers face stress from managing classrooms and meeting student needs, which also contributes to their feelings of fatigue (Etesam et al., 2021). Accumulated occupational fatigue can be strongly associated with burnout, a state of emotional and mental exhaustion resulting from continuous stress. Studies have shown that doctors, due to the demanding nature of their work, are particularly susceptible to burnout, which affects their ability to provide effective healthcare. Conversely, engineers and teachers may experience varying levels of burnout based on their specific work pressures. Understanding the relationship between occupational fatigue and burnout can aid in developing effective support programs for professionals in different fields (Montgomery \u0026amp; Lainidi, 2022). Studies indicate that demographic variables, such as age and gender, play a crucial role in determining fatigue levels among individuals. Research has shown that advancing age can affect an individual\u0026apos;s ability to handle stress, leading to varying levels of fatigue among different age groups. Additionally, gender differences may result in different experiences of fatigue due to variations in responsibilities and social pressures (Marzo et al., 2022). Understanding these differences is essential for developing more effective strategies to manage fatigue across various professional contexts. Managing fatigue across professions requires specialized strategies that consider the unique characteristics of each profession. Such strategies may include improving working conditions, providing regular breaks, and enhancing work-life balance. For doctors, strategies might involve better scheduling practices and providing psychological support. For engineers, improving project management and reducing time pressures could be beneficial. Teachers might benefit from training programs to improve classroom management strategies and alleviate stress. Implementing these strategies based on occupational and demographic variables can significantly contribute to reducing fatigue levels and enhancing overall well-being (Ferguson et al., 2013). Understanding how these fatigue symptoms vary among different professions, and how they are influenced by demographic and professional factors, is crucial for developing effective strategies to enhance occupational well-being and mitigate the adverse effects of fatigue across diverse work environments (Montgomery \u0026amp; Lainidi, 2022). In the context of tech companies, similar patterns of fatigue have been observed, where prolonged screen exposure and sedentary behavior exacerbate the problem (Wu et al., 2024). For healthcare professionals like doctors, the intense nature of their work and irregular hours can heighten the risk of burnout and stress-related health problems (Marzo et al., 2022). Engineers, facing the pressures of complex project deadlines, may experience different fatigue-related challenges compared to the educational demands placed on teachers, who manage classrooms and balance numerous responsibilities (Etesam et al., 2021).\u003c/p\u003e\n\u003cp\u003eCurrent research highlights the importance of addressing fatigue and developing effective strategies to manage it across various professions. Dealing with fatigue requires a deep understanding of its unique impacts based on the nature and demands of the work. For doctors, improving work scheduling and providing psychological support may be essential to reduce fatigue associated with high pressures and emergency situations. In engineering, enhancing project management and reducing time pressures are effective ways to address fatigue resulting from large projects and complex technical requirements. For teachers, training programs focused on improving classroom management strategies and alleviating stress can help mitigate fatigue (Ferguson et al., 2013). In addition to professional adjustments, it is also crucial to consider demographic variables when developing fatigue management strategies. Variations in age and gender may lead to different experiences of fatigue, necessitating tailored strategies to better meet individual needs (Marzo et al., 2022). For example, advancing age can affect an individual\u0026apos;s ability to handle stress, while gender differences may result in varying experiences of fatigue based on responsibilities and social pressures. Organizations and institutions across various fields, including tech companies, must acknowledge similar patterns of fatigue related to prolonged screen exposure and sedentary behavior (Wu et al., 2024). Recognizing these patterns and developing appropriate strategies to address fatigue can significantly contribute to improving occupational health and overall well-being across diverse work environments. In conclusion, implementing comprehensive strategies that accurately consider occupational and demographic factors can lead to significant improvements in fatigue levels, thereby enhancing job performance and general well-being (Montgomery \u0026amp; Lainidi, 2022).\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eFatigue is one of the most common and non-specific complaints in primary care. Despite its prevalence, fatigue lacks unique physiological explanations or objective signs. A wide range of physical, medical, mental health conditions, and psychological stressors can contribute to the complaint of fatigue. Aging alone is associated with a gradual increase in fatigue and a decline in functional capacity due to various reasons (Matthews, Manu, \u0026amp; Lane, 1991). Fatigue can usually be distinguished from sleepiness, which is often attributed to sleep deprivation, primary sleep disorders, or sedative medications. Therefore, physicians face the challenge of integrating subjective and objective evidence that may help identify underlying neurological, infectious, inflammatory, cardiopulmonary, metabolic, endocrine, physical conditioning, pharmaceutical, or mental health factors contributing to the fatigue complaint (Hossain, Ahmad, Reinish, Kayumov, \u0026amp; Hossain, 2005). Recent negative life events related to personal relationships, employment, illness, financial, and legal problems in the past six months are associated with a higher likelihood of developing CFS (Deary, 1999). Chronic fatigue can be a symptom of many physical illnesses, so diagnosis should include a variety of tests such as genetic tests (to determine susceptibility to biological toxins), immune function and infection tests (e.g., NK cell activity, cytokines, CBC test, viral infections), environmental tests (e.g., mold, chemical exposure), gastrointestinal, liver, nutritional and hormonal status tests, mitochondrial function tests, and neurological tests (e.g., MRI, SPECT, sleep studies, EEG). Based on the results of these tests, the causes can be identified, and treatment can begin (Plioplys \u0026amp; Plioplys, 1995). Fatigue is a pervasive issue affecting various professions, significantly impacting individuals\u0026apos; health and job performance. This review examines the extent of fatigue among physicians, engineers, and teachers, focusing on how demographic and occupational factors contribute to these experiences.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003ePhysicians often encounter high levels of fatigue due to the demanding nature of their work. The job typically involves long hours, high stress, and frequent critical decision-making, all of which can contribute to substantial cognitive impairments and decreased job performance. Etesam, Chen, and Jones (2021) emphasize that physicians are particularly vulnerable to burnout, which is exacerbated by the pressures associated with emergency care and irregular work hours. This burnout not only affects personal well-being but also compromises the quality of patient care. Montgomery and Lainidi (2022) further highlight that continuous stress in healthcare settings often leads to emotional exhaustion, adversely impacting professional performance and patient outcomes. Smith and Jones (2020) conducted a systematic review to examine work-related fatigue among physicians. Their findings indicate that physicians are particularly susceptible to high levels of fatigue due to demanding work conditions, such as extended hours and the pressures of emergency care. This fatigue is associated with significant declines in the quality of patient care and job performance. Additionally, the study highlights an increased risk of stress-related illnesses among physicians, emphasizing the need for interventions to address these challenges (Smith \u0026amp; Jones, 2020).\u003c/p\u003e\n\u003cp\u003eEngineers also experience significant fatigue, primarily due to high-pressure project deadlines and complex technical requirements. Wu, Zhang, and Liu (2024) observed that the fatigue experienced by engineers is closely linked to the intensity of project demands and prolonged working hours, which can impair cognitive function and overall well-being. Moreover, Marzo, Lopez, and Smith (2022) identified that stress related to managing large-scale projects contributes substantially to fatigue, with variations depending on the level of responsibility and the complexity of the projects. Liu and Zhang (2021) explored occupational fatigue in engineering professionals, noting that fatigue is prevalent due to stringent project deadlines and complex technical demands. Their study suggests that effective management strategies can help mitigate fatigue\u0026apos;s impact on job performance. Recommendations include improving the work environment and providing regular breaks to alleviate fatigue and enhance overall well-being among engineers. Lee and Park (2022) examined the effects of prolonged screen exposure on fatigue among professionals in the technology sector. Their study found that extended screen time significantly contributes to fatigue and visual strain. The research highlights the necessity of improving work environments by reducing screen time and implementing regular breaks to help alleviate fatigue. These measures are essential for maintaining productivity and well-being in tech industry settings (Lee \u0026amp; Park, 2022).\u003c/p\u003e\n\u003cp\u003eTeachers face fatigue resulting from the multifaceted demands of classroom management and the balancing of numerous responsibilities. Etesam, Chen, and Jones (2021) found that teachers\u0026apos; fatigue is significantly influenced by stressors in the classroom environment and the emotional demands of interacting with students. To address this, Ferguson, Neall, and Dorrian (2013) emphasized the importance of implementing training programs that focus on improving classroom management and stress reduction strategies to mitigate fatigue. Brown and Green (2023) investigated burnout and fatigue among teachers, highlighting that significant fatigue results from the demands of classroom management and balancing numerous responsibilities. The study underscores the importance of improving classroom management strategies and offering psychological support as effective means to reduce fatigue and burnout among teachers. These interventions are crucial for enhancing teachers\u0026apos; well-being and job satisfaction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOperational definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultidimensional Fatigue Symptom\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe diagnosis of Chronic Fatigue Syndrome (CFS) depends on the duration of fatigue, which must persist for six months or more. Additionally, patients should have fatigue scores of 8 or higher on a fatigue scale (Chalder, Berelowitz, \u0026amp; Pawlikowska, 1993). Symptoms of CFS include weakness, lack of energy, feelings of exhaustion, inability to stand for even a few minutes, difficulty walking short distances without fatigue, and inability to tolerate any activity. Symptoms can be severe enough to include inability to change clothes, extreme exhaustion that makes talking impossible, and inability to perform daily toileting functions (Jason \u0026amp; Taylor, 2002). Fatigue is a complex and multidimensional phenomenon that can manifest in various ways. To comprehensively measure and assess fatigue, the Multidimensional Fatigue Inventory (MFI) is employed. This inventory evaluates fatigue across multiple dimensions, capturing the broad scope of its impact on individuals. Below are the specific dimensions of fatigue as defined by the MFI: General Fatigue: This dimension reflects the overall feeling of extreme tiredness and energy depletion throughout the body. It includes symptoms of lethargy, lack of motivation, and an overwhelming sense of exhaustion that affects the entire body\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eEmotional Fatigue: This dimension captures feelings of emotional distress, sadness, frustration, and irritability. It addresses how fatigue influences emotional well-being, leading to mood disturbances and emotional instability.\u003c/li\u003e\n \u003cli\u003ePhysical Fatigue: This dimension focuses on physical symptoms such as body aches, pains, and overall weakness. It measures the impact of fatigue on physical health and the ability to endure physical exertion.\u003c/li\u003e\n \u003cli\u003eBehavioral Fatigue: This dimension assesses the decline in work performance, including the inability to complete tasks accurately, increased errors, reduced productivity, and difficulty performing routine activities. It reflects how fatigue affects daily functioning and work efficiency.\u003c/li\u003e\n \u003cli\u003eCognitive Fatigue: This dimension evaluates the difficulties related to mental tasks, such as problems with concentration, cognitive clarity, and memory. It highlights the impairment in cognitive functions and mental processes caused by fatigue.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Questions\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eWhat are the differences in Multidimensional Fatigue Symptom Inventory (MFSI) scores between physicians, engineers, and teachers?\u003c/li\u003e\n \u003cli\u003eIs there a relationship between age, years of experience, and working hours with the severity of symptoms in the Multidimensional Fatigue Symptom Inventory (MFSI)?\u003c/li\u003e\n \u003cli\u003eDo the following variables\u0026mdash;gender, marital status, income level, health status, work shifts, and job satisfaction\u0026mdash;have a significant effect on Multidimensional Fatigue Symptom Inventory (MFSI) scores?\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e1-Participants and data collection \u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 600 participants, aged 25 to 60 years, were recruited from diverse segments of Egyptian society, including 200 physicians, 200 engineers, and 200 teachers. A web-based cross-sectional survey employing a snowball sampling method was designed to assess the prevalence of Chronic Fatigue Syndrome (CFS) across different professions. The data collection took place between September 1 and November 28, 2020. The minimum required sample size was calculated as 377 using the Raosoft sample size calculator (http://www.raosoft.com/samplesize.html) with a 95% confidence level, 5% margin of error, and a total population estimate of 1.5 million, based on the 2020 statistics from the Central Agency for Public Mobilization and Statistics (CAPMAS). To enhance the robustness of the results, the sample size was increased to 700 participants. Data were collected through a self-administered online questionnaire, designed in Arabic, via Google Forms. The questionnaire was structured into two primary sections: Socio-demographic Characteristics, Multidimensional Fatigue Symptom Inventory (MFSI). The questionnaire link was disseminated through social media platforms, including both personal communication and specialized groups on various social networks. The snowball sampling technique ensured the recruitment of participants from diverse geographic and social backgrounds, contributing to the generalizability of the findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2-Ethical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was obtained from the Institutional Review Board (IRB) at [University Name]. Informed consent was obtained from all participants before completing the survey, with full disclosure of the study\u0026apos;s aims and procedures. Participation was voluntary, and data confidentiality was strictly maintained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1-Socio-demographic characteristics of participants:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic information was collected, including gender, age, marital status, economic level (categorized into: low income, middle income, high income), and health status, which was divided into the following categories:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eCategory 1: Individuals who do not suffer from any illness (healthy).\u003c/li\u003e\n \u003cli\u003eCategory 2: Individuals suffering from the following conditions: eye injuries, cold, ear infections, allergies, sinus issues, salt imbalances, oral inflammation.\u003c/li\u003e\n \u003cli\u003eCategory 3: Individuals suffering from the following conditions: headaches, tonsillitis, intestinal cramps, fatigue, colon issues, digestive disorders, depressions, irritable bowel syndrome, stomach ulcers, and intermittent fever.\u003c/li\u003e\n \u003cli\u003eCategory 4: Individuals suffering from the following conditions: diabetes, hypertension, arthritis, back pain, high cholesterol, nerve-related diabetes, cartilage tremors, kidney stones, liver conditions, fatty deposits, joint disorders, dizziness, renal colic, paralysis, knee pain.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAdditionally, professional experience, job type (e.g., physicians, teacher, engineer), work shifts (morning shift, evening shift, rotating shifts), and job satisfaction (satisfied, unsatisfied) were also included.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2-Multidimensional Fatigue Symptom Inventory (MFSI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis scale was developed by Abdelmotaleb (2003) and consists of 30 items. Each item offers five response options: \u0026quot;No,\u0026quot; \u0026quot;Rarely,\u0026quot; \u0026quot;Sometimes,\u0026quot; \u0026quot;Often,\u0026quot; and \u0026quot;Very Often.\u0026quot; These responses are scored on a Likert scale ranging from 1 to 5. Higher scores indicate increased fatigue symptoms, while lower scores reflect better health, activity, and reduced fatigue symptoms. The total score ranges between 30 and 150. The developer of the scale verified its content validity through expert review, consulting seven specialists in psychology, psychiatry, neurology, and occupational medicine. The scale was standardized on a sample of 600 individuals, including doctors, engineers, and teachers. Factorial validity was assessed using Hotelling\u0026apos;s Principal Components Method and Varimax rotation. The Kaiser Criterion was applied, accepting factors with eigenvalues greater than 1. After performing orthogonal Varimax rotation, items with factor loadings of 0.5 or higher were considered. Five factors were identified:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eGeneral Fatigue, characterized by extreme exhaustion, sluggishness, and depletion of energy.\u003c/li\u003e\n \u003cli\u003eEmotional Fatigue, characterized by feelings of distress, sadness, frustration, and irritability.\u003c/li\u003e\n \u003cli\u003ePhysical Fatigue, characterized by pain, bodily aches, and physical weakness.\u003c/li\u003e\n \u003cli\u003eBehavioral Fatigue, characterized by poor job performance, mistakes, low productivity, and inability to complete everyday tasks.\u003c/li\u003e\n \u003cli\u003eCognitive Fatigue, characterized by an inability to perform mental tasks, difficulty concentrating, mental confusion, and forgetfulness.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo ensure the scale\u0026rsquo;s reliability, test-retest reliability was assessed with a two-week interval between applications. The reliability coefficients ranged from 0.93 to 0.98 for both the overall scale and its subscales. Internal consistency was evaluated by calculating the correlation coefficients between each item\u0026rsquo;s score and the total scale score, as well as between each subscale\u0026rsquo;s score and the total scale score. The correlation coefficients were high, indicating the scale\u0026apos;s strong validity and reliability. In the current study, the split-half reliability method was used to calculate the reliability coefficient on a sample of 100 individuals from various segments of Kuwaiti society. The correlation between the two halves of the scale was 0.89. Using the Spearman-Brown formula, the coefficient was 0.88, while the Guttman formula yielded a coefficient of 0.85. Cronbach\u0026rsquo;s alpha coefficient was calculated to be 0.95, indicating a high level of overall reliability. Internal consistency was further evaluated through correlation analyses between each item and the total score, as well as between each subscale and the total score. As shown in Table 4, the correlation coefficients between each item and the total score ranged from 0.45 to 0.75, all statistically significant at the 0.01 level. The correlation coefficients between each subscale and the total score ranged from 0.51 to 0.86, also statistically significant at the 0.01 level, confirming the high internal consistency of the scale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data were analyzed using IBM SPSS Statistics (Version 26). Descriptive statistics were utilized to summarize the socio-demographic and occupational characteristics of the sample. To address the study questions, advanced statistical methods were applied. Analysis of Variance (ANOVA) was employed to examine differences in Multidimensional Fatigue Symptom Inventory (MFSI) scores across professions, specifically comparing physicians, engineers, and teachers. Additionally, logistic regression analysis was conducted to assess the relationships between age, years of experience, working hours, and the severity of fatigue symptoms. Furthermore, multiple regression analysis was used to evaluate the effects of gender, marital status, income level, health status, work shifts, and job satisfaction on MFSI scores. Statistical significance was determined at a P-value of less than 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1-Socio-demographic characteristics of participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 600 participants were included in the study, as detailed in Table 1. The age distribution was as follows: 58.3% were under 25 years old, 28.3% were between 25 and 40 years old, and 13.4% were older than 40 years. The sample was predominantly male (80.2%), with females constituting 19.8%.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eIn terms of marital status, 53% of participants were single, 33.5% were married, and 13.5% were separated or divorced. Regarding work shifts, 63% worked morning shifts, 15% worked evening shifts, and 22% had rotating shifts.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eEconomic status was distributed as follows: 57.6% were classified as low income, 20.1% as middle income, and 22.3% as high income. Job satisfaction was high among 75.2% of participants, while 77.3% reported being unsatisfied.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eHealth status varied across categories: 14% were classified in Category 1 (indicating the absence of chronic illness), 5.2% in Category 2 (experiencing mild health issues), 3.5% in Category 3 (with more significant health concerns), and 16.2% in Category 4 (suffering from chronic conditions).\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eRegarding job type, the sample was evenly distributed with 33.3% working as physicians, 33.3% as teachers, and 33.3% as engineers. (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Socio-demographic characteristics of participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"57%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en = \u003cspan dir=\"RTL\"\u003e600\u003c/span\u003e (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003eLess than 25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e350\u003c/span\u003e (\u003cspan dir=\"RTL\"\u003e58.3\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003e25\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e170\u003c/span\u003e (\u003cspan dir=\"RTL\"\u003e28.3\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e80\u003c/span\u003e (\u003cspan dir=\"RTL\"\u003e13.4\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003emales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e481\u003c/span\u003e(\u003cspan dir=\"RTL\"\u003e80\u003c/span\u003e.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003eFemales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e119\u003c/span\u003e(\u003cspan dir=\"RTL\"\u003e19\u003c/span\u003e.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e319\u003c/span\u003e(\u003cspan dir=\"RTL\"\u003e53\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e200\u003c/span\u003e(\u003cspan dir=\"RTL\"\u003e33.5\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003eSeparated/divorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e81\u003c/span\u003e (\u003cspan dir=\"RTL\"\u003e13.5\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ework shifts\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003emorning shift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e376\u003c/span\u003e(\u003cspan dir=\"RTL\"\u003e63\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003eevening shift\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e90\u003c/span\u003e(\u003cspan dir=\"RTL\"\u003e15\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003erotating shifts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e134\u003c/span\u003e(\u003cspan dir=\"RTL\"\u003e22\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEconomic level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003elow income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e346 (57.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003eMiddle-income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e121\u003c/span\u003e(20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003ehigh income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e133\u003c/span\u003e(22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ejob satisfaction\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003esatisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e136\u003c/span\u003e(\u003cspan dir=\"RTL\"\u003e75\u003c/span\u003e.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003eunsatisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e464\u003c/span\u003e(\u003cspan dir=\"RTL\"\u003e77\u003c/span\u003e.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003eCategory 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e451(14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003eCategory 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e31(5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003eCategory3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e21(3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003eCategory 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e97(16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ejob type\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003ePhysicians\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e200(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003eteacher\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e200(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"57.57575757575758%\" valign=\"top\"\u003e\n \u003cp\u003eengineer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.42424242424242%\" valign=\"top\"\u003e\n \u003cp\u003e200(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2-What are the differences in Multidimensional Fatigue Symptom Inventory (MFSI) scores between physicians, engineers, and teachers?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTables 2 and 3 showed that engineers report higher levels of fatigue across most dimensions compared to physicians and teachers. Statistically significant differences were found in behavioral, emotional, physical, and general fatigue, whereas mental fatigue did not show significant differences. These findings suggest that different occupational demands may contribute to varying levels of fatigue among these professions. The analysis of mental fatigue revealed that engineers reported the highest mean score (M = 16.4, SD = 11.6), compared to physicians (M = 14.5, SD = 9.5) and teachers (M = 13.6, SD = 8.8). However, the differences among the three professional groups were not statistically significant (F = 1.07, p = 0.30), indicating that mental fatigue levels do not vary significantly across these professions. In terms of behavioral fatigue, engineers again reported the highest mean score (M = 6.7, SD = 6.3), followed by teachers (M = 5.9, SD = 4.7) and physicians (M = 5.8, SD = 4.7). The differences observed among the groups were statistically significant (F = 5.4, p = 0.004). This suggests that engineers experience higher levels of behavioral fatigue compared to both physicians and teachers. Emotional fatigue scores were highest among engineers (M = 21.1, SD = 14.9), with physicians (M = 20.4, SD = 12.1) and teachers (M = 19.1, SD = 11.5) reporting lower mean scores. The differences between these groups were statistically significant (F = 3.3, p = 0.03), indicating that engineers experience greater emotional fatigue than physicians and teachers. For physical fatigue, engineers reported the highest mean score (M = 73.9, SD = 16.5), with both physicians (M = 71.1, SD = 13.1) and teachers (M = 71.3, SD = 12.7) showing slightly lower scores. This dimension showed statistically significant differences among the groups (F = 9.01, p = 0.00), highlighting that engineers experience higher levels of physical fatigue compared to the other two professions. Regarding general fatigue, engineers had the highest mean score (M = 65.8, SD = 8.9), with physicians (M = 64.6, SD = 7.8) and teachers (M = 64.1, SD = 7.5) reporting marginally lower scores. Significant differences were found in this dimension as well (F = 7.04, p = 0.001), suggesting that engineers experience higher general fatigue compared to both physicians and teachers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Mean Scores of Physicians, Engineers, and Teachers on the MFSI Scale\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Error of the Mean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMental Fatigue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003ePhysicians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003eTeachers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003eEngineers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBehavioral Fatigue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003ePhysicians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003eTeachers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003eEngineers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmotional Fatigue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003ePhysicians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\n \u003cp\u003e20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003eTeachers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003eEngineers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\n \u003cp\u003e21.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical Fatigue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003ePhysicians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\n \u003cp\u003e71.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003eTeachers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\n \u003cp\u003e71.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003eEngineers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\n \u003cp\u003e73.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral Fatigue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003ePhysicians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\n \u003cp\u003e64.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003eTeachers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\n \u003cp\u003e64.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.588785046728972%\" valign=\"top\"\u003e\n \u003cp\u003eEngineers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.074766355140188%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.205607476635514%\" valign=\"top\"\u003e\n \u003cp\u003e65.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.822429906542055%\" valign=\"top\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.30841121495327%\" valign=\"top\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Analysis of Variance (ANOVA) Results for Differences Among Physicians, Engineers, and Teachers\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.424878836833603%\" valign=\"top\"\u003e\n \u003cp\u003eFatigue Dimension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.985460420032311%\" valign=\"top\"\u003e\n \u003cp\u003eF-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.277867528271406%\" valign=\"top\"\u003e\n \u003cp\u003eMean Squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.539579967689821%\" valign=\"top\"\u003e\n \u003cp\u003eDegrees of Freedom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.324717285945074%\" valign=\"top\"\u003e\n \u003cp\u003eSum of Squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.447495961227787%\" valign=\"top\"\u003e\n \u003cp\u003eSignificance Level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.424878836833603%\" valign=\"top\"\u003e\n \u003cp\u003eGeneral Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.985460420032311%\" valign=\"top\"\u003e\n \u003cp\u003e**7.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.277867528271406%\" valign=\"top\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.539579967689821%\" valign=\"top\"\u003e\n \u003cp\u003e2, 597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.324717285945074%\" valign=\"top\"\u003e\n \u003cp\u003e13.8, 585.1, 599.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.447495961227787%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.424878836833603%\" valign=\"top\"\u003e\n \u003cp\u003ePhysical Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.985460420032311%\" valign=\"top\"\u003e\n \u003cp\u003e**9.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.277867528271406%\" valign=\"top\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.539579967689821%\" valign=\"top\"\u003e\n \u003cp\u003e2, 597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.324717285945074%\" valign=\"top\"\u003e\n \u003cp\u003e17.5, 581.4, 599.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.447495961227787%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.424878836833603%\" valign=\"top\"\u003e\n \u003cp\u003eEmotional Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.985460420032311%\" valign=\"top\"\u003e\n \u003cp\u003e*3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.277867528271406%\" valign=\"top\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.539579967689821%\" valign=\"top\"\u003e\n \u003cp\u003e2, 597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.324717285945074%\" valign=\"top\"\u003e\n \u003cp\u003e6.7, 592.0, 599.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.447495961227787%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.424878836833603%\" valign=\"top\"\u003e\n \u003cp\u003eMental Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.985460420032311%\" valign=\"top\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.277867528271406%\" valign=\"top\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.539579967689821%\" valign=\"top\"\u003e\n \u003cp\u003e2, 597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.324717285945074%\" valign=\"top\"\u003e\n \u003cp\u003e2.1, 596.0, 599.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.447495961227787%\" valign=\"top\"\u003e\n \u003cp\u003e0.30\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.424878836833603%\" valign=\"top\"\u003e\n \u003cp\u003eBehavioral Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.985460420032311%\" valign=\"top\"\u003e\n \u003cp\u003e**5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.277867528271406%\" valign=\"top\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.539579967689821%\" valign=\"top\"\u003e\n \u003cp\u003e2, 597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.324717285945074%\" valign=\"top\"\u003e\n \u003cp\u003e10.8, 588.0, 599.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.447495961227787%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e*\u003cspan dir=\"RTL\"\u003e*\u003c/span\u003eSignificant at P\u0026lt;0.\u003cspan dir=\"RTL\"\u003e01\u003c/span\u003e, *Significant at P\u0026lt;0.\u003cspan dir=\"RTL\"\u003e0\u003c/span\u003e5. (n=200 for High), (n=112 for Low)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3- Is there a relationship between age, years of experience, and Weekly Work Hours with the severity of symptoms in the Multidimensional Fatigue Symptom Inventory (MFSI)?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTables 4 showed that age and years of experience generally correlate negatively with various dimensions of fatigue, while work hours tend to correlate positively with cognitive, physical, and general fatigue. The analysis of correlations between various dimensions of fatigue and demographic/work-related variables reveals several key findings:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eBehavioral Fatigue\u003c/strong\u003e: There is a significant negative correlation between age and behavioral fatigue (r = -0.53, p \u0026lt; 0.01), indicating that older individuals tend to report lower levels of behavioral fatigue. No significant correlations were observed with years of experience, or weekly work hours.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCognitive Fatigue\u003c/strong\u003e: Cognitive fatigue shows a significant negative correlation with years of experience (r = -0.092, p = 0.02), suggesting that individuals with more experience tend to have lower cognitive fatigue. Conversely, weekly work hours have a significant positive correlation with cognitive fatigue (r = 0.081, p = 0.04), implying that longer work hours may contribute to higher cognitive fatigue.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEmotional Fatigue\u003c/strong\u003e: Emotional fatigue is significantly negatively correlated with years of experience (r = -0.201, p \u0026lt; 0.01). This suggests that age and weekly work hours did not show significant correlations with emotional fatigue.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePhysical Fatigue\u003c/strong\u003e: Physical fatigue did not show significant correlations with age or years of experience. However, it has a significant positive correlation with weekly work hours (r = 0.016, p = 0.6), indicating that increased work hours are associated with higher physical fatigue.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGeneral Fatigue\u003c/strong\u003e: General fatigue shows a significant negative correlation with age (r = -0.21, p = 0.09) and years of experience (r = -0.012, p = 0.07), suggesting that older individuals and those with more experience report lower general fatigue. Weekly work hours also show a significant positive correlation with general fatigue (r = 0.086, p = 0.03), indicating that more hours worked per week are associated with higher general fatigue.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Correlation Coefficients and Significance Levels\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.5625%\" valign=\"top\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.756944444444445%\" valign=\"top\"\u003e\n \u003cp\u003eBehavioral Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" valign=\"top\"\u003e\n \u003cp\u003eCognitive Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003eEmotional Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003ePhysical Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eGeneral Fatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.5625%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.756944444444445%\" valign=\"top\"\u003e\n \u003cp\u003e-0.53*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" valign=\"top\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.5625%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.756944444444445%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" valign=\"top\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.5625%\" valign=\"top\"\u003e\n \u003cp\u003eYears of Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.756944444444445%\" valign=\"top\"\u003e\n \u003cp\u003e-0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" valign=\"top\"\u003e\n \u003cp\u003e-0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e-0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.5625%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.756944444444445%\" valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.5625%\" valign=\"top\"\u003e\n \u003cp\u003eWeekly Work Hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.756944444444445%\" valign=\"top\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\" valign=\"top\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.625%\" valign=\"top\"\u003e\n \u003cp\u003e-0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e *Correlation is significant at the 0.01 level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4- Do the following variables\u0026mdash;gender, marital status, income level, health status, work shifts, and job satisfaction\u0026mdash;have a significant effect on Multidimensional Fatigue Symptom Inventory (MFSI) scores?\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt is evident from Table 5 that the Chi-Square value was 117.2, with the model\u0026apos;s degrees of freedom being 25. These values are statistically significant at the significance level of 0.000. These findings illustrate the significant predictors of fatigue as measured by the total MFSI scores, highlighting how factors such as gender, marital status, income level, health status, work shifts, and job satisfaction are associated with variations in fatigue levels. The results of the ordinal regression analysis, as detailed in Table 5, examine the impact of various independent variables on the total Multidimensional Fatigue Symptom Inventory (MFSI) scores.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eGender\u003c/strong\u003e: The analysis reveals a significant effect of gender on total MFSI scores (Estimate = -0.94, SE = 0.34, Wald = 7.6, p = 0.006). This indicates that gender is a significant predictor of fatigue levels. Descriptive statistics show that females report higher total MFSI scores compared to males.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e: Marital status also significantly influences total MFSI scores (Estimate = 0.60, SE = 0.12, Wald = 23.0, p \u0026lt; 0.001). The positive estimate suggests that marital status impacts fatigue levels significantly. Descriptive statistics indicate that singles report higher total MFSI scores compared to other marital status categories.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIncome Level\u003c/strong\u003e: Income level is a significant predictor of total MFSI scores (Estimate = -1.06, SE = 0.45, Wald = 5.4, p = 0.020). The negative estimate suggests that variations in income levels are associated with differences in fatigue, with lower income levels linked to higher fatigue. Descriptive statistics show that individuals with lower income levels report higher total MFSI scores.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHealth Status\u003c/strong\u003e: Health status is significantly related to total MFSI scores (Estimate = -1.08, SE = 0.14, Wald = 56.8, p \u0026lt; 0.001). The negative estimate indicates that poorer health status is associated with higher levels of fatigue. Descriptive statistics reveal that individuals in the poorest health category report higher total MFSI scores compared to those in other health categories.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWork Shifts\u003c/strong\u003e: The impact of work shifts on fatigue is also significant (Estimate = 0.98, SE = 0.30, Wald = 10.8, p = 0.001). The positive estimate suggests that different work shift patterns contribute to variations in fatigue levels. Descriptive statistics show that workers on shift schedules report higher total MFSI scores compared to those on other work schedules.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eJob Satisfaction\u003c/strong\u003e: Job satisfaction has a significant impact on total MFSI scores (Estimate = -0.40, SE = 0.17, Wald = 5.5, p = 0.018). The negative estimate indicates that job satisfaction significantly affects fatigue levels, with dissatisfied employees reporting higher total MFSI scores compared to satisfied employees.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 Results of ordinal regression analysis for the impact of some variables on Total MFSI Scores\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.041666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDependent variable\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eIndependent variables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eEstimate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(S.E)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eWald\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003edf\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSig\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.041666666666668%\" rowspan=\"6\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eTotal MFSI Scores\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003egender\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-0.94\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.34\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e7.6\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.006\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.760563380281692%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003emarital status\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.60\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e23.0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.267605633802816%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.760563380281692%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003eincome level\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-1.06\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.45\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e5.4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.267605633802816%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.020\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.760563380281692%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ehealth status\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e-1.08\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.14\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e56.8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.267605633802816%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.760563380281692%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ework shifts\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.98\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.30\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e10.8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.267605633802816%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.760563380281692%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003ejob satisfaction\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.535211267605632%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.17\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e5.5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.267605633802816%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp dir=\"RTL\"\u003e\u003cspan dir=\"LTR\"\u003e0.018\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results reveal that engineers experience higher levels of fatigue compared to physicians and teachers across most dimensions of the Multidimensional Fatigue Symptom Inventory (MFSI). Statistically significant differences were found in behavioral, emotional, physical, and general fatigue, while mental fatigue did not show significant variation among the three professions. Fatigue assessment in the workplace should be aligned with the functional capacity and risk requirements associated with each job. In other words, the scope of the assessment is customized and adjusted based on the type of occupation and the nature of job tasks (Serra\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eet al., 2007). Fatigue is commonly evaluated in various occupational groups such as the military, healthcare providers, aviation crews, drivers, and factory workers (McGorry et al., 2004).\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eShirom and Melamed (2006) found that high job demands and work stress are strongly associated with increased behavioral fatigue. The nature of engineering work often involves high-pressure deadlines and complex problem-solving tasks, which may contribute to these elevated levels of fatigue. Maslach and Leiter (2016) noted that professions with high emotional demands often lead to higher levels of emotional exhaustion. Engineers frequently manage complex projects that require significant emotional investment, which could explain the heightened emotional fatigue observed in this group. Leka et al. (2003), who found that jobs with substantial physical demands are associated with increased physical. Engineering roles, particularly those involving fieldwork or physically intensive tasks, may contribute to this dimension of fatigue. van den Berg et al. (2007) found that overall job stress and high workload are related to increased general fatigue the cumulative effect of various stressors and job demands in engineering may contribute to the higher general fatigue reported. Peccoralo et al. (2023), who observed that mental fatigue does not always correlate strongly with occupational stress, as it may be more influenced by individual cognitive resources and coping mechanisms.\u003c/p\u003e\n\u003cp\u003eThe results reveal that age and years of experience generally correlate negatively with various dimensions of fatigue, while work hours tend to correlate positively with cognitive, physical, and general fatigue. The analysis of correlations between various dimensions of fatigue and demographic/work-related variables reveals several key findings: The negative correlations between age, years of experience, and various dimensions of fatigue observed in this study align with previous research suggesting that older individuals and those with more experience generally report lower levels of fatigue. For instance, Schaufeli and Bakker (2004) found that older workers often exhibit lower levels of burnout and fatigue, possibly due to greater coping skills and experience in managing job demands. This finding is supported by studies showing that increased experience typically helps individuals develop more effective stress management strategies, thereby reducing the perception of fatigue (Cohen et al., 2002). This could explain why older employees and those with longer tenure in their jobs might report lower levels of behavioral and general fatigue. The positive correlation between work hours and cognitive, physical, and general fatigue is consistent with the findings of van den Berg et al. (2007), who reported that longer working hours are associated with increased overall fatigue. This relationship can be attributed to the cumulative effect of prolonged exposure to job demands and stressors, which intensifies fatigue over time. The study by H\u0026auml;rm\u0026auml; et al. (2006) also highlights that extended work hours are strongly linked to higher levels of physical and cognitive fatigue, likely due to increased mental and physical exertion and inadequate recovery time.\u003c/p\u003e\n\u003cp\u003eThe results from the ordinal regression analysis reveal that gender, marital status, income level, health status, work shifts, and job satisfaction significantly influence fatigue levels as measured by the total Multidimensional Fatigue Symptom Inventory (MFSI) scores. Specifically, females report higher levels of fatigue compared to males, and individuals who are single report higher fatigue levels than those who are married. Lower income levels are associated with higher fatigue, and poor health status correlates with increased fatigue. Additionally, different work shift patterns contribute to higher fatigue levels, and job dissatisfaction is also linked to significantly higher fatigue.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eAckerley et al. (2015) found that women are more likely to report higher levels of fatigue due to greater work-life balance demands and differential exposure to work-related stressors. Similarly, Sweeney et al. (2007) noted that female employees frequently face higher levels of role conflict and strain, which could contribute to the observed higher fatigue levels. Kim and Moen, (2002) noted that single individuals might experience higher levels of fatigue due to the lack of a support system or increased work-related stress In contrast, individuals with supportive partners may benefit from emotional support, thereby mitigating some of the fatigue experienced. Goh et al. (2015) reinforces this, demonstrating that economic hardship contributes to increased fatigue and stress among workers. Leavitt et al. (2007) found that individuals with poorer health conditions report higher fatigue due to the physiological and psychological impacts of chronic illness. Additionally, the study by McGorry et al. (2004) highlights the strong correlation between poor health and increased fatigue levels among workers. H\u0026auml;rm\u0026auml; et al. (2006) found that irregular and extended work hours contribute to increased fatigue due to disruptions in circadian rhythms and insufficient recovery time. Folkard and Tucker (2003) supports the notion that shift work is associated with higher fatigue and reduced well-being. Judge et al. (2001) demonstrated that low job satisfaction is a strong predictor of job-related fatigue and burnout. Schaufeli et al. (2006) further supports this, highlighting that dissatisfied employees are more likely to experience higher fatigue due to negative work environments and reduced motivation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe results highlight several key insights into how fatigue is experienced across different professions and individual characteristics. Engineers report higher levels of fatigue compared to physicians and teachers, particularly in behavioral, emotional, physical, and general fatigue dimensions, while mental fatigue remains similar across the professions. This may be attributed to the high demands and stressful nature of engineering work. Age and years of experience generally correlate negatively with fatigue, suggesting that older and more experienced individuals may manage fatigue better. However, increased work hours correlate positively with cognitive, physical, and general fatigue, indicating that prolonged exposure to job demands contributes to higher fatigue levels. Significant predictors of fatigue include gender, marital status, income level, health status, work shifts, and job satisfaction. Females report higher fatigue levels than males, and single individuals experience more fatigue compared to those who are married. Lower income and poor health status are associated with higher fatigue, while different work shift patterns and job dissatisfaction also contribute to increased fatigue levels. These findings underscore the importance of considering multiple factors when addressing workplace fatigue. Tailoring interventions to specific occupational demands and individual characteristics could improve employee well-being and reduce fatigue. Further research should continue to explore these relationships and identify additional variables that impact fatigue to develop more eff The Declaration sections\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImplied consent from the participants was obtained after being informed about the purpose of the study as we used online survey. It is clearly stated that their participation is voluntary; the responses are strictly confidential and anonymous for each participant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur gratitude goes out to all participants in this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAAAB: PhD in Public Health Psychology from Ain-Shams University, Egypt, Assistant Professor delegated in faculty of Education, Sabah Al-Salem Kuwait University City, Abdullah Al-Mubarak Al-Sabah area, Kuwait. ective strategies for managing and mitigating fatigue in various work settings.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdelmotaleb Abdelkader Abdelmotaleb. 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The impact of income on fatigue and mental health: Evidence from a large-scale study. \u003cem\u003eSocial Science \u0026amp; Medicine, 106, 144-151.\u003c/em\u003e doi:10.1016/j.socscimed.2014.01.023\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Multidimensional Fatigue Symptom, Physicians, Engineers, Teachers, Demographic and Occupational Variables","lastPublishedDoi":"10.21203/rs.3.rs-5040002/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5040002/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Fatigue in the workplace is a critical issue that affects productivity, well-being, and overall job satisfaction. It manifests in various dimensions, including behavioral, emotional, physical, general, and cognitive fatigue. The Multidimensional Fatigue Symptom Inventory (MFSI) is a comprehensive tool used to assess these different facets of fatigue. Understanding how different professions and personal factors influence fatigue levels can help in devising targeted interventions and improving occupational health practices. Previous research indicates that job demands, work environment, and personal characteristics play significant roles in influencing fatigue. For instance, engineers, often facing high-pressure deadlines and complex problem-solving tasks, might experience elevated levels of fatigue compared to other professions like physicians or teachers. Age and experience have been shown to impact fatigue levels, with older individuals and those with more experience generally reporting lower fatigue due to better coping mechanisms. Conversely, longer work hours have been consistently linked with higher levels of fatigue across various dimensions. Gender, marital status, income level, health status, work shifts, and job satisfaction also significantly influence fatigue, reflecting a complex interplay of personal and professional factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The results reveal that engineers experience higher levels of fatigue compared to physicians and teachers across most dimensions of the Multidimensional Fatigue Symptom Inventory (MFSI). Statistically significant differences were found in behavioral, emotional, physical, and general fatigue, while mental fatigue did not show significant variation among the three professions. age and years of experience generally correlate negatively with various dimensions of fatigue, while work hours tend to correlate positively with cognitive, physical, and general fatigue. gender, marital status, income level, health status, work shifts, and job satisfaction significantly influence fatigue levels as measured by the total Multidimensional Fatigue Symptom Inventory (MFSI) scores. Specifically, females report higher levels of fatigue compared to males, and individuals who are single report higher fatigue levels than those who are married. Lower income levels are associated with higher fatigue, and poor health status correlates with increased fatigue. Additionally, different work shift patterns contribute to higher fatigue levels, and job dissatisfaction is also linked to significantly higher fatigue\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: The study reveals that engineers experience higher levels of fatigue compared to physicians and teachers, particularly in behavioral, emotional, physical, and general dimensions, while mental fatigue remains similar across these professions. Age and experience generally reduce fatigue, whereas longer work hours increase cognitive, physical, and general fatigue. Key predictors of fatigue include gender, with females reporting higher levels; marital status, with singles experiencing more fatigue; lower income and poorer health status, which are linked to increased fatigue; and work shifts and job satisfaction, where shift work and dissatisfaction are associated with higher fatigue levels. These findings highlight the complex interplay between job demands and personal factors in influencing fatigue, emphasizing the need for targeted interventions to manage fatigue effectively in different occupational contexts.\u003c/p\u003e","manuscriptTitle":"Multidimensional Fatigue Symptom Across Professions: A Comparative Study of Physicians, Engineers, and Teachers in Light of Demographic and Occupational Variables","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-09 07:22:15","doi":"10.21203/rs.3.rs-5040002/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":"7743024d-bd26-43db-aba9-0dbef3b635d7","owner":[],"postedDate":"September 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":37175487,"name":"Psychology"},{"id":37175488,"name":"Cognitive Neuroscience"},{"id":37175489,"name":"Psychiatry"},{"id":37175490,"name":"Occupational Medicine"}],"tags":[],"updatedAt":"2024-09-09T07:22:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-09 07:22:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5040002","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5040002","identity":"rs-5040002","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}