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Soonchunhyang Med Sci > Volume 19(2); 2013 > Article
Lee: Fatigue and Job Stress in University Hospital Nurses

ABSTRACT

Objective:

To investigate fatigue-related risk factors among university hospital nurses.

Methods:

A total of 309 subjects were analyzed among university hospital nurses and interviewed by well trained interviewer using a structured questionnaire and fatigue related factors to gather information on demographics, social and work characteristics, fatigue symptoms, and job stress related to their occupation. Multiple regression modeling was used to test the assessment of fatigue- related risk factors.

Results:

A significant increase in fatigue was observed in the high stress group related to job demand, organizational system, lack of reward, and occupational climate than in the low group (P<0.001; P=0.008; P=0.045; P=0.003, respectively). In a multiple regression analysis, fatigue was associated with job demand, insufficient job control, and occupational climate (P=0.007; P=0.024; P<0.001, respectively). Fatigue was associated with work department, although a borderline statistical significance was seen (P=0.005).

Conclusion:

To prevent fatigue in university hospital nurses, it is important to consider work department, job contents, and working environment related to job stress. Preventive strategies to lower fatigue will be needed under the consideration of job stress related to job demand, insufficient job control and occupational climate for university hospital nurses.

INTRODUCTION

With the rapid development of the industry and a more complicated industrial system, fatigue at work is a normal everyday experience. However, in the case of severe fatigue it may affect the person’s performance in the working as well as the home setting. Severe long term fatigue may lead to sick leave and work disability. Fatigue can be closely affected by public health, economical, and social aspect as well as medical aspect. Generally, it was reported that fatigue in the working population could lower work productivity and increase medical utility and maladjustment in a daily life, resulting in increasing social costs. Although studies on patients suffered from fatigue have been reported, a few studies on general population and working population have been conducted.
Nursing is the working environment that is notable for its high work demands, high job stress and high level of fatigue [1,2]. Hospital nursing includes a directly medical service for patients and a continuing contact with patients’ families. Hospital nurses can provide health services in cooperation with other hospital workers including hospital staffs and paramedical staffs. Recently the medical environment has been changed rapidly with big flood of information, resulting in rising medical needs, expanding work expectation for hospital nursing, and increasing job demands for nurses. Compared with 11.9 nurses and midwives per 1,000 people in Sweden, 10.4 in Canada and 24.0 in Finland, the number of Korean nurses and midwives was 5.3 per 1,000 people [3], indicating that Korea has the relatively lower level among Organization for Economic Cooperation and Development nations. Especially for fatigue we must question the possibility of an inverse relation: the fatigue leading to a negative judgment on work instead of adverse working conditions leading to fatigue. High job stress can influence mentally and physically negative impacts to nurses. Occupational errors or accidents involving nurses have a direct and critical influence on the life and prognosis of their patients as such it is clear that this is yet another important issue that must be addressed. Moreover, prevention of medical errors and accidents is an urgent issue to be addressed from the viewpoint of occupational health [4-8].
Here, the primary aim of this study was to identify fatigue and its related factors in full-time working female nurses. Second, we aimed to provide important basic information for establishing preventive strategies for hospital nurses.

MATERIALS AND METHODS

1. Study subjects

This study was conducted at a university hospital and a total of 500 nurses were identified as the study population. Of the 500 nurses who were invited to participate in the study, a total of 346 nurses agreed to participate (response rate: 69.2%). The self-administered questionnaire included items on sociodemographics, personal medical history, smoking, alcohol drinking habit, and work related factors. Excluded from the study were those who reported missing data (n=27), or were men (n=10). Finally, 309 eligible subjects were included in the analysis.

2. Data collection

Data were collected using a structured questionnaire by well trained interviewer from February to April 2007. All of the study subjects were given a verbal explanation of the study procedures before giving their written informed consent. This study included questions on sociodemographics, personal medical history, smoking and alcohol drinking habits, and work related factors.
The fatigue symptom was estimated using nineteen items of the Multidimensional Fatigue Scales (MFS). It reconstructed twentynine items of the MFS which were developed by Schwarz et al. [9] and undergone evaluation as a standardized tool with a high reliability [10]. Items consisted of three subscales of the MFS, including general fatigue (8 items), daily dysfunction (6 items) and situational fatigue (5 items). All items used a seven-point Likert response set, ranging from 1 to 7. Reported Cronbach’s alpha was 0.93 for the scale [11] and 0.82 for this study in the reliability test.
Twenty-four items of the short form of the Korean Occupational Stress Scales (KOSS-SF) were used to estimate job stress in the working setting [12]. Items were scored using conventional 1-2-3-4 Likert scores for the response categories, ranging from 1=not at all to 4=always. Seven subscales were included in KOSS as follows: job demand (4 items), insufficient job control (4 items), interpersonal conflict (3 items), job insecurity (2 items), organizational system (4 items), lack of reward (3 items), and occupational climate (4 items). The levels of the subscales of occupational stress were divided by using a median as the high and low group.

3. Statistical analysis

Descriptive statistics were used to examine the distribution of demographic, social, and occupational characteristics. Cronbach’s alpha reliability coefficients were used to estimate internal consistency reliability of the instruments. One-way analysis of variance and t-test were used to estimate the mean scores of fatigue according to demographic, social, work characteristics, and job stress in the analysis. Multiple regression modeling was used to examining fatigue-related risk factors in the study subjects. Statistical analysis was performed using SPSS ver. 14.0. (SPSS Inc., Chicago, IL, USA). All P-values were based on two-sided tests and were considered to be statistically significant at less than 0.05.

RESULTS

Table 1 shows background characteristics of the study subjects. The overall mean score of fatigue was 85.0 (SD=21.0). About 53% were between 20 and 29 years of age, 32% were between 30 and 39 years of age, and 14.6% were older than 40 years, indicating that the majority were 20s and 30s of age. A significant increase in the mean score of fatigue was shown in the younger age group rather than the older (P= 0.005). Significant differences of the mean fatigue scores were observed in marital status and in physical work burden (P= 0.029; P<0.001, respectively). There were significant differences between work departments, indicating that the highest level was general wards and special units and out-patient department were in the order (P<0.001). The majority of work duration was less than 10 years (58.9%). The mean scores of fatigue were significantly high in less than 10 years (P= 0.002).
Table 2 shows the mean scores of fatigue by self-rated fatigue in the study subjects. The lowest mean scores were 76.8 in the group reporting being ‘same as usual’ and the highest scores were 97.6 in the group reporting being ‘very higher than usual’ (P<0.001). Table 3 shows distribution of job stress score for the study subjects. The average score of total job stress was 47.7 (SD=9.3). In the analysis of sub categories, the highest category was job demand and organizational system and insufficient job control were in the order.
To compare the differences of fatigue level by subcategories of job stress, significant differences were observed in job demand, organizational system, lack of reward, and occupational climate, indicating that the high group of job stress was significantly higher in job demand, organizational system, lack of reward, and occupational climate compared to the low group of job stress (P<0.001; P= 0.008; P= 0.045; P= 0.003, respectively). The mean score of overall job stress was significantly high in the high group than in the low group of job stress (P= 0.017) (Table 4). To estimate fatiguerelated risk factors, a multiple regression analysis was performed in the study subjects. Fatigue was associated with work department, although a borderline statistical significance was seen (P= 0.055). Fatigue was associated with job demand, insufficient job control and occupational climate (P= 0.007; P= 0.024; P<0.001, respectively) (Table 5).

DISCUSSION

This study suggested that fatigue was associated with job stress among Korean university hospital nurses. We observed that the average score of total job stress was 47.7 (SD=9.3), indicating that it was consistent with previous study of hospital nurses [13]. In an analysis of subcategories, the highest level was observed in job demand. Consistently with previous studies [8,13], the mean score of job demand was 62.5. Compared with 58.6 in a previous study of Chang et al. [12], the finding indicated that nurses have high job stress due to excess work load and high job demand in hospital settings. Recent reports revealed that job-related stress such as high levels of job insecurity and lack of reward were related to an increase in depressive symptoms among Korean nurses [14] and effort-reward imbalance at work (imbalance between work demanding and rewards such as esteem, job promotion, and job security) was associated with depressive symptoms in Chinese nurses [15]. Furthermore, the demand for medical services has increased rapidly in Korea, resulting in spending a great deal of time in excess work load and high job stress. Therefore, a detailed job analysis of nurses will be needed to improve the working environment and to achieve a proper human resources management.
Fatigue can be affected by a variety of reasons. Previous studies [16,17] concluded that a significant increase in fatigue was observed in the younger age group and in the shorter working duration, consistently with this study, indicating that their groups were relatively lower in job stability and work adjustment. In addition, previous researches revealed that fatigue was associated with working type such as overtime [18] as well as stress [19,20]. We found that fatigue was significantly associated with job demand, insufficient job control and occupational climate in university hospital nurses. In an analysis of subscales, the highest level of job stress was observed in job demand and the second highest level in organizational system, indicating that work characteristics of nursing could have a relatively high level of unpredictability and insufficient job control. These findings were consistently with previous reports from Korean nurses [8,13]. Moreover, because there are diverse job types in hospital settings, hospital nurses should provide various medical services in cooperation with hospital workers.
One limitation of this study is the cross-sectional design, because it cannot establish a cause-and effect relationship between fatigue-related risk factors. Another limitation was the measurement of symptoms and other variables using a questionnaire because it may make the possibility of subjective response. Finally, other potential confounding variables were not evaluated. Therefore, a more comprehensive study evaluating fatigue levels both at the workplace and in daily life should be carried out in the future. Notwithstanding the known limitations, we thought that subjective responses of the study subjects could not perfectly removed on investigating our interest variables because those variables such as fatigue and work stress have subjective character. Further investigation will be needed to establish preventive strategies for hospital nursing.
To prevent fatigue in hospital nurses, it is important to consider job contents and occupational environment related to job stress. Preventive strategies will be needed to reduce fatigue with consideration of job stress related to job demand, insufficient job control and occupational climate for hospital nurses.

Table 1.
Mean scores of fatigue by background characteristics in the study subject
Characteristic N (%) Fatigue
Mean±SD P-valuea)
Age (yr) 0.005**
 20-29 164 (53.1) 88.1±21.3
 30-39 100 (32.4) 83.6±20.8
 ≥40 45 (14.6) 77.1±18.1
Marital state 0.029*
 Single 177 (57.3) 87.3±20.7
 Married 132 (42.7) 82.0±21.1
Physical work burden <0.001***
 No 136 (44.0) 80.0±23.0
 Yes 173 (56.0) 89.0±18.4
Housework (hr) 0.184
 <1 149 (48.2) 86.7±20.9
 ≥1 160 (51.8) 83.5±21.0
Past history of injury 0.311
 No 203 (65.7) 85.9±20.4
 Yes 106 (34.3) 83.3±22.2
Work department <0.001***
 General wards 138 (44.7) 88.8±18.6
 Special units 98 (31.7) 85.7±18.8
 Out-patient department 73 (23.6) 77.0±25.7
Work duration (yr) 0.002**
 <5 110 (35.6) 88.0±20.5
 5-9 72 (23.3) 89.6±21.2
 10-14 58 (18.8) 83.0±21.7
 15-19 44 (14.2) 79.0±16.9
 ≥20 25 (8.1) 74.2±22.3
 Total 309 (100.0) 85.0±21.0

a) By unpaired t-test and one way analysis of variance.

* P< 0.05.

** P< 0.01.

*** P< 0.001.

Table 2.
Mean scores of fatigue by self-rated fatigue
Variable Value P-valuea)
Same as usual (n=136) 76.8±19.0
Slightly higher than usual (n=137) 89.9±21.4
Very higher than usual (n=36) 97.6±13.8 <0.001***

Values are presented as mean± SD.

a) By one way analysis of variance.

*** P< 0.001.

Table 3.
Distribution of job stress score for the study subjects
Independent variable Mean±SD Median
Job demand 62.5±14.8 66.7
Insufficient job control 52.8±16.3 50.0
Interpersonal conflict 36.1±13.5 33.3
Job insecurity 41.0±18.8 33.3
Organizational system 54.5±14.6 50.0
Lack of reward 47.6±15.4 44.4
Occupational climate 39.3±15.5 41.7
Total job stress 47.7±9.3 47.2
Table 4.
Relationship between job stress and fatigue in 309 subjects
Independent variable Fatigue
Value P-valuea)
Job demand Low (n=152) 79.8±19.4 <0.001***
High (n=157) 90.1±21.3
Insufficient job control Low (n=111) 84.5±21.7 0.735
High (n=198) 85.3±20.7
Interpersonal conflict Low (n=41) 83.2±21.7 0.555
High (n=268) 85.3±20.9
Job insecurity Low (n=50) 83.7±29.4 0.711
High (n=259) 85.3±19.1
Organizational system Low (n=81) 79.7±20.5 0.008**
High (n=228) 86.9±20.9
Lack of reward Low (n=110) 81.8±20.8 0.045*
High (n=199) 86.8±21.0
Occupational climate Low (n=146) 81.3±21.6 0.003**
High (n=163) 88.4±19.9
Total job stress Low (n=156) 82.2±20.8 0.017*
High (n=153) 88.0±20.9

Values are presented as mean± SD.

a) By unpaired t-test.

* P< 0.05.

** P< 0.01.

*** P< 0.001.

Table 5.
Multiple regression model predicting fatigue in the study subjects
Independent variable Fatigue
95% CI
β SE P-value
Constant 76.696 13.172 <0.001 50.772 to 102.620
Work duration 0.088 2.293 0.970 -4.427 to 4.599
Work department
 General wards 5.776 3.004 0.055 -0.136 to 11.689
 Special units 3.832 3.070 0.213 -2.210 to 9.874
Job stress
 Job demand 0.226 0.082 0.007 0.063 to 0.388
 Insufficient job control -0.179 0.079 0.024 -0.334 to -0.023
 Interpersonal conflict 0.020 0.095 0.830 -0.166 to 0.207
 Job insecurity 0.033 0.064 0.610 -0.093 to 0.158
 Organizational system 0.015 0.101 0.882 -0.184 to 0.214
 Lack of reward 0.093 0.094 0.320 -0.091 to 0.278
 Occupational climate 0.346 0.083 <0.001 0.183 to 0.509
R2 0.225
Adjusted R2 0.188

Adjusted for age, marital status, household hour, and physical work burden.

SE, standard error; CI, confidence interval.

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