Occupational burnout and its related factors in nurses in University of Medical Sciences of Shahrood

Transcription

1 Occupational burnout and its related factors in nurses in University of Medical Sciences of Shahrood *Sakine Ashrafi Department of management, Shahrood Branch, Islamic Azad University, Shahrood, Iran Mehdi Sanei Department of management, Shahrood Branch, Islamic Azad University, Shahrood, Iran Abstract Occupational burnout is a syndrome in which causes a negative self-image, negative attitude toward the job and relationship with clients, leads to severe loss of quality of health services. The health service sector is one of the most important fields of sustainable development of human society, in which needs therapists who are healthy, highly enthused and motivated. This paper is a descriptive exploratory study, and aims to evaluate occupational burnout and its related factors in nurses in University of Medical Sciences of Shahrood. The study population is 410 nurses of University of Medical Sciences in To assess the opinions of the respondents in the survey, researcher s questionnaire of occupational burnout is used. The results of factor analysis showed that the factors affecting nurses burnout include: job stress, job dissatisfaction, physical environment, burnout and economic factors. In addition, tests related to the demographic variables showed that the opinions of the respondents are the same on the factors affecting occupational burnout in terms of age, marital status, work experience and work shift. Single-sample T-test results showed that the factor of "low wages" has the highest priority in burnout in the workplace, and "rapid advance of technology and lack of the ability to go along with it," is the lowest priority of burnout in the workplace. The results of multiple regression analysis showed that job stress has the greatest impact on the incidence of burnout among nurses in University of Medical Sciences of Shahrood. Keywords: occupational burnout, job stress, job dissatisfaction, burnout. Page 1392

2 1. Introduction Job is set of tasks and related, consistent and clear responsibilities, which is recognized as a unit work by the organization. In fact, job is one of the reasons for stress in people s life. Job is considered as the formation of social identity, source of satisfying life needs and forms social relationships for everyone (Delpasand, 2010: 10). Physical and psychological stress caused by job are the including ones that if they be excessive, they can cause physical, mental, behavioral, and personal difficulties and finally jeopardize health. In addition, the existence of such threats along with pressures on organizations can reduce the quality of one's goals (Aghajani, 2012: 103). In jobs classification, some are difficult and dangerous. These include jobs in which physical, chemical, mechanical, and biological factors of work environment are non-standard, and they cause tension far higher than the natural capacity (physical and mental) in the person. The consequence is illness and burnout in employees; Jobs such as working in mines, digging canals and wells, ongoing works in the open air and with height of more than 5 meters above the ground on poles, towers and structures and scaffolding, welding operations inside tanks, spraying orchards and trees, working with vibrating tools as if they are dangerous for health, jobs that cause diseases by radiation, and working in hospitals (Payami, 2000: 52). Difficult and dangerous jobs lead to occupational burnout. Burnout is a phenomenon, which can affect any human in systems. Therefore, they experience a feeling of inability to do things, feelings of inadequacy, loss of interest, unwillingness and inability to maintain balance in starting a job. Irritability, feelings of hopelessness and the willingness to self-medication with painkillers and sedatives can be seen in patients with burnout syndrome. In fact, burnout is a physical-mental syndrome with fatigue, which leads to negative behavior and attitude towards work, clients, nonproductive work and absents in work, poor ethics and lack of job satisfaction (Rafie, 2012: 28). Long-term stress can lead to burnout. Many experts believe that the prevalence of burnout among nurses is because of stressful situations arising from the structure of the organization, such as role ambiguity, role conflict and work pressure and lack of positive conditions in the workplace (Embriaco, 2007). Therefore, due to the importance of the subject we decided to study occupational burnout in nurses in University of Medical Sciences of Shahrood and its related factors to provide and suggest effective interventions through the obtained results for the managers and policy makers to control or eliminate the causes of burnout. Theoretical definition of burnout: Atef (2006) was the first person whom validated the term in its modern sense. He considers burnout as an exhaustion and fatigue that arises by hard work with no provocation and interest. Furthermore, he believed that burnout syndrome shows itself in different symptoms. These symptoms and their severity vary from one person to another, and begin usually after one year when the person is working in a department or agency (Atef, 2006). Operational definition of burnout: The operational definition is the score in which each of the variables obtains through the questionnaire. In this study, a burnout questionnaire was used, which consists of 28 questions and is based on the Likert scale. 2. Research Methodology The present study is an applied-exploration research. And in terms of data collection is a survey research. Page 1393

3 The population: I. A total of 410 nurses in University of Medical Sciences of Shahrood. Sample Size: The sample size is determined through Cochran formula for the survey and research process. Considering these assumptions and on the basis of the formula, the statistical sample size was calculated as follows: Table 1-3: The introduction of variables in sample size formula (Cochran) n number of samples N The total number of population in this study as equal to 410 people. P The proportion of those who own the expected attribute, in this research it is 0/5. q The proportion of those who do not own the expected attribute, in this research it is 0/5. ԑ Maximum limit error or the estimation accuracy which is considered to be equivalent to 5%. Critical number of Normal distribution is at 95 percent. For this study, we consider 95 percent for confidence. Therefore, α would be equal to 0/05. Based on the above assumptions and the formula, sample size will be equivalent to the 199 numbers. The sample was randomly selected among nurses in the mentioned university. Questionnaire is a common tool for survey and is a direct method to obtain research data. Questionnaire is set of questions (phrases or statements), that respondent answers by reviewing them. The answers provide the researchers required data. Questionnaire s questions can be considered as stimulus-response. Through questionnaires one can assess the knowledge, interests, attitudes, and opinions related to specific issues in an individual (Moghimi, 2001: 29). Questionnaire s questions consist of two parts: 1. General Questions: in general questions, it has been tried to collect adequate and demographic information in connection with respondents. This part includes 7 questions (age, gender, work experience, marital status, level of education, type of employment and work shifts). 2- Specific questions: This part includes 28 questions. The design of this part is tried to be understandable as much as possible. To design this part of five-point Likert scale was used. General shape and ratings of Likert scale in this questionnaire is as follows: )1( Very low )2( low )3( medium )4( high )5( Very high After compiling the draft questionnaire, the validity and reliability was determined. 2-1 validity He term "validity" is derived from the word "valid" meaning correct and true. The purpose of the term "validity" is that the measuring tool be able to measure the characteristics and features. The importance of validity is because inadequate measurements can make any scientific research worthless and unduly. In this analysis KMO test and Bartlett's test are used to check the validity of the research. KMO index shows the sampling adequacy, and is ranged from zero to one. If it be closer to one, the underlying data for are better suited for factor analysis; otherwise, (generally less than 0.6) factor analysis is not very good. The index is derived from the following Page 1394

4 equation where is the correlation coefficient between variables of i and j, and is the partial correlation coefficient between them: Bartlett's test also checks whether the correlation matrix is known, and so it is not appropriate to identify the structure of operating model. If sig. in Bartlett's test is less than 5%, factor analysis to identify the operating model is appropriate. In Table 1, Bartlett's test results which is approximately equal to chi-square is shown. Bartlett's test sig. value is less than 5% (0.000). It shows that the factor analysis to identify the structure factor model is fine. And assuming that the correlation matrix is known would be rejected. The KMO index has the value of Since the amount of KMO is close to one, the sample size is sufficient for factor analysis. Table 1. Bartlett's test results KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy Bartlett's Test of Sphericity Approx. Chi-Square df 323 Sig Table 2, shows the percentage, variance and eigenvalues of different factors. Here, factors with eigenvalues of 1 5 are greater than 1 and remain in the analysis. If we look at the relative cumulative variance column, these 5 factors can explain % of the variability (variance) of variables. Therefore, the validity obtained by factor analysis for the questionnaire is more than 65 percent. Table 2. The percentage, variance and eigenvalues of different factors Total Variance Explained Extraction Sums of Squared Rotation Sums of Squared Initial Eigenvalues Loadings Loadings % of % of Compone nt Total Varianc e Cumulativ e % Total Varianc e Cumulativ e % Tota l % of Varianc Cumulativ e e % Page 1395

5 Reliability One of the characteristics of measurement tools (questionnaire), is its reliability. The concept of this term is to determine how much a tool in the same condition gives the same results. Reliability which is also regarded as trusting, and accuracy, is when a measurement tool show similar results in similar circumstances when measuring the variables and attributes. In other words, a reliable tool has equal reproducibility and results measurements. In this study, Cronbach s test was used. Cronbach s Alpha coefficient is calculated using the following formula: 2 n i 1 2 n 1 t n: number of questionnaires 2 : The variance of responses to each question i 2 t : Variance of each sample response Obtained coefficient of is a number between zero and one, and as it is closer to one, the questionnaire is more reliable. If this coefficient is small (less than 0/5), the questions that have led to this small coefficient should be removed or corrected in the questionnaire. To ensure the reliability of the questionnaires distributed among employees, SPSS statistical software was used to calculate the Cronbach s alpha coefficient. The value was 0/937. Thus, it was concluded that the reliability of the questionnaire is accepted. Page 1396

6 3- Findings According to the research subject, a questionnaire containing 28 questions about occupational burnout was distributed among nurses in university if medical sciences of Shahrood. Then the obtained data from questionnaires was put into quantitative analysis by using SPSS software, and research hypotheses were investigated by using statistical tests. In this chapter, the preliminary findings from the distributed questionnaires were put into descriptive and inferential analysis. First, descriptive characteristics of the study population have been discussed, and then by using analytical methods as mentioned in the previous chapter, other analyses have been performed. Inferential statistics includes factor analysis and parametric tests such as one-sample T-test, two samples T-test, multi-sample T-test and multiple regression analysis. 3-1 research hypotheses tests (factor analysis test) In Table 3, Bartlett's test results which is approximately equal to chi-square is shown. Bartlett's test sig. value is less than 5% (0.000). It shows that the factor analysis to identify the structure factor model is fine. And assuming that the correlation matrix is known would be rejected. The KMO index has the value of Since the amount of KMO is close to one, the sample size is sufficient for factor analysis. Table 3. Bartlett's test results and KMO value KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy Bartlett's Test of Sphericity Approx. Chi-Square df 378 Sig Table 4 shows the initial and extraction communalities. Communality of a variable is squared multiple correlation (R 2 ) for the corresponding variables by using the factors as predictor. The first column states the communalities before extraction. As a result all communalities are equal to 1. In the second column, the larger the values of extraction communalities, the better the extracted factors display the variables. If any of the extraction values are very small (less than 0.5), they should be removed. Another factor extraction may be required. Here, questions 3 and 13 have communalities values less than 0.5. Thus, the questions are removed in the second round of the factor analysis. Table 4 shows the initial and extraction communalities Communalities Initial Extraction question question question question question question Page 1397

7 question question question question question question question question question question question question question question question question question question question question question question Table 5 shows the percentage, variance and eigenvalues of different factors. Here, factors with eigenvalues of 1 5 are greater than 1 and remain in the analysis. If we look at the relative cumulative variance column, these 5 factors can explain % of the variability (variance) of variables. In addition, in the rotation of the remaining factors, the proportion of total variance explained by the five factors is fixed. Unlike non-rotation method, the first factor determines greater percentage of changes ( percent). In factors rotation method, each of them approximately explains equal proportion of changes. This feature is called Varimax rotation, which distributes the changes among factors equally. Table 5. The percentage, variance and eigenvalues of different factors Total Variance Explained Extraction Sums of Rotation Sums of Initial Eigenvalues Squared Loadings Squared Loadings % of Varianc e Cumulati ve % Total % of Varianc e Cumulati ve % Tota l Compone nt Total % of Varianc Cumulati e ve % Page 1398

8 We conducted re-analysis of variance with the removal of questions 3 and 13, and check le its output. Table 6 shows the amount of communalities in none of the variables is less than 0/5. So, reliability check is now finished. Table 6. Initial and extraction communalities values in the second round of factor analysis Communalities Initial Extraction Question Question question question question question Page 1399

9 question question question question question question question question question question question question question question question question question question question question Table 5 shows the percentage, variance and eigenvalues of different factors. Here, factors with eigenvalues of 1 5 are greater than 1 and remain in the analysis. If we look at the relative cumulative variance column, these 5 factors can explain % of the variability (variance) of variables. The table shows that the total variance (65.097) is slightly increased compared to the previous step. Table 7. The percentage, variance and eigenvalues of different factors in the second round of factor analysis Total Variance Explained Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Component Total % of Variance Cumulative % Total % of Variance Cumulative % % of Total Variance Cumulative % Page 1400

10 Graph 1 is a scree graph for variables. The graph is a graphic illustration of the eigenvalue of each extracted factors. On the vertical axis, eigenvalues, and on the horizontal axis the number of components are available. The amount of variance (eigenvalues) is decreasing after the fifth factor. Eigenvalues of the first to fifth factors is more than one and therefore are remained in the output. Graph 1. Scree graph Page 1401

11 Table 8 shows the components matrix or non-rotated factors, including factor loadings (factor scores) of each variables in 5 remaining factors. Table 8. Components matrix or non-rotated factors Component Matrix Component Question question Question Question question Question Question question question question question question question question question question question question question question question Question question Question Question Question Interpretation of factor loadings without rotation is not easy, so factors will be rotated, to increase their ability to be interpreted. This issue can be seen in the next output. Table 9 shows the components rotated matrix rotated, including factor loadings of each variable in the 5 remaining rotated factors. This matrix can be interpreted easier than the previous matrix. The more the absolute value of the coefficient, the greater role the relevant factor in total variance has. Page 1402

12 Table 9 components rotated matrix Rotated Component Matrix Component Question Question question question question question question question question question question question question question question question question question question question question question question question question question According to the result of factor analysis on the remaining 26 variables, five factors were identified as the key factors. - Factor analysis showed that the variables (questions) 4, 12, 15, 17, 18, 19, 20, 21, 22, 23, 24 and 28 are placed under the first factor. - Variables (questions) 2, 5, 8, 9 and 10 are placed under the second factor. - Variables (questions) 25, 26 and 27 are placed under the third factor. - Variables (questions), 7, 11, 14 and 16 are placed under the fourth factor. - Variables (questions) 1 and 6 are placed under the fifth factor. These 5 factors have been named as follows according to a literature review: 1) The first factor: stress, 2) second factor: job dissatisfaction, 3) third factor: the physical environment, 4) fourth factor: burnout, 5) fifth factor: economic factors Page 1403

13 Thus: Lack of adequate facilities and equipment, emotional exhaustion in emergency situations, discrimination in work, role ambiguity, role overload, weak role, conflict between colleagues, role incongruence, a large amount of responsibility, delegation of authority, lack of financial and emotional support from colleagues and rapid advance of technology and the inability to go along with it are considered as the first factor and titled as job stress. Variables of high` working hours, high workload, mandatory overtime work, sudden changes in the patients conditions and the second factor and working shift and nightshift are considered as the second factor and titled as job dissatisfaction. Variables of harmful chemicals to health in the workplace, ergonomic factors harmful to health in the workplace and physical factors harmful to health in the workplace are considered as the second factor and titled as physical environment. Neglecting the nurses position, conflict with inexperienced doctors, repeated exposure to the suffering of patients and lack of supporting reasonable complaints of nurses and staff are considered as fourth factor and titled as the burnout. Variables of low salary payments and sense of injustice are considered as the fifth factor and titled as economic factors. 3-2 The results of Kolmogorov Smirnov test First, to make sure whether to use parametric tests or non- parametric ones for hypotheses testing, Kolmogorov - Smirnov test was conducted to determine components normality of the model. The hypotheses include: H 0 = distribution is normal H 1 = distribution is not normal The results can be seen in Table 10. As can be seen, in component of occupational burnout, the sig (0.120) is greater than 5%. As a result, hypothesis H 0 is accepted. So normal distribution is confirmed, so parametric tests will be used. Table 10. Results of Kolmogorov Smirnov test One-Sample Kolmogorov-Smirnov Test occupational burnout N 211 Normal Parameters Mean Std. Deviation Most Extreme Differences Absolute Positive Negative Kolmogorov-Smirnov Z Asymp. Sig. (2-tailed) Determine the importance of factors in the workplace One-sample T-test was used to test the hypothesis. To see if one sample belongs to a particular community or not. The hypotheses are: Page 1404

14 μ μ Table 11 provides the results of one sample T-test. Test results show that the average value of sample in all the questions is bigger than the average of response spectrum (3). But this assumption must be verified through inferential statistics (hypothesis testing or confidence interval). Sig value is in all questions. Since the sig is smaller than 5%, H0 is rejected. To determine the importance of factors, the column of mean is used. In the table below on the tight, the mean value is given in terms of questions, and on the left side, these values are sorted from highest to lowest. As can be seen "low salary" is the highest priority of burnout in the workplace. And a "rapid advance of technology and the inability to go along with it" is the lowest priority of burnout in the workplace. Table 11. One-sample T-test Not sorted sorted questions mean Standard deviation t DOF Sig level questions mean 1 question Question question question question question question question question Question question Question question Question question question question Question question Question question Question question Question question question question question question question question question question Question question Question question question question Question question Question question Question question Question question question question question question Question Page 1405

15 3-4- Multiple regression analysis Multiple regression equation with five dependent variables is defined as follows. The dependent variable of (y) is as burnout. The independent variables are: job stress ( ), job dissatisfaction ( ), physical environment ( ), burnout ( ) and economic factors ( ). Table 12 shows the entered independent variables, the removed variables, and the method used to determine the regression (Enter). Enter is an approach in variable selection, in which all the entered variables are used in one step in determining the regression. Table 12. Entered / removed variables Variables Entered/Removed Variables Model Variables Entered Method Removed Job stress, job dissatisfaction, physical environment, 1. Enter burnout and economic factors One of the hypothesis that is considered in the regression, is independence of the errors (the difference between the actual values and the predicted values by the regression equation) from each other. If the hypothesis of independence of the errors is rejected and errors are correlated with each other, there is no possibility of using the regression. In order to evaluate the independence of the errors, Durbin Watson test is used. Table 13 shows the multiple correlation coefficient, coefficient of determination, adjusted coefficient of determination, standard error of the estimate and Durbin Watson statistics. Since the Durbin - Watson (1/983) is in the range of 1/5 to 2/5, so hypothesis of no correlation in errors will be accepted, and regression can be used. Table 13. R values and Durbin Watson Model Summary Model R R Adjusted R Std. Error of the Durbin- Square Square Estimate Watson Graph 2 shows a normal distribution of errors. By comparing Frequency distribution of errors and normal distribution graph, it is observed that the distribution of errors is almost normal, so regression can be used. In addition, the average value provided in the right side of the graph is very small (close to zero) and standard deviation is close to one. Figure 2. Normal distribution of errors Page 1406

16 Graph 3 is also used to check the status of normal distribution of errors. In this graph, points which are located on the diameter show that the cumulative probability is equal to the expected cumulative probability. In fact, as points are more accumulated around the diameter, the dependent variable can be predicted more accurately. This graph also can be used to assess the normality of variables. So that, as points are more accumulated around the diameter, and closer to diameter, it represents variables normality. Graph 3, linear graph of the normal distribution of errors Page 1407

17 Table 14 contains regression analysis to determine the certainty of a linear relationship between variables. In this table, the source of dependent variable in the regression and the residual is shown. And for each of these two sources, the sum of squares, degrees of freedom and the mean square are presented. Here, as sum of squares for residual is smaller than the sum of the squares for regression, it shows explanatory power of the model in explaining dependent variable. On the other hand, sig (0.000) is less than 5 percent so that the linearity of the model is confirmed. Table 14. Regression variance analysis ANOVA Model Sum of Squares df Mean Square F Sig. Regression Residual Total The regression coefficients and the fixed values are provided in Table 15 and in column B, respectively. Other columns include: standard error of coefficients in column B, beta (standardized coefficient value, which indicates the rate of change in the dependent variable for changes equal to one standard deviation of the independent variable) as its absolute value is bigger, it shows stronger relationship between the dependent variable and independent variables. Sig and t are presented to test this hypothesis that each of the coefficients in Column B are equal to zero. Since, sig is less than 5 percent, so the hypothesis that regression coefficients are equal to zero is rejected. So, there is no need to remove them from the regression equation. In other words, these five independent variables influence the dependent variables. In addition, sig is greater than 5% (0.468). So, the hypothesis, in which the fixed value is equal to zero will be accepted, so it will be eliminated from the regression equation. Table 15. Regression coefficients Coefficients Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta ( Fixed amount ) Job stress Job dissatisfaction The physical environment Burnout Economic factors Thus the regression equation is: As we have stated, this study is based on the data collected through questionnaires, and were analyzed through parametric tests using SPSS software to test the hypotheses. Page 1408

18 4. Conclusion At first, factor analysis was used to identify the factors affecting occupational burnout among nurses in University of Medical Sciences of Shahrood. The results showed that the index value of KMO is equal to 0.906, so the sample size (number of respondents) is sufficient for factor analysis. In addition, in this test questions 3 and 13 had communalities values less than 0.5. Thus, the questions were removed in the second round of the factor analysis. According to the result of factor analysis on the remaining 26 variables, five factors were identified as the key factors. These 5 factors, have been named as follows according to a literature review: 1) The first factor: stress, 2) second factor: job dissatisfaction, 3) third factor: the physical environment, 4) fourth factor: burnout, 5) fifth factor: economic factors Results of Kolmogorov Smirnov test showed that the questionnaire s sig is generally greater than 5 percent and normality of the questionnaires is accepted. Therefore, parametric tests were used to analyze the data. In order to determine the importance of the factors in the workplace, one-sample T-test was used. The results showed that in all questions, responses average was greater than response spectrum (3) and sig value is smaller than 5%. To determine the importance of factors, the mean column was used. As can be seen "low salary" is the highest priority of burnout in the workplace. And "rapid advance of technology and the inability to go along with it" is the lowest priority of burnout in the workplace. Research results by Masoudi et al. (2008) showed that the main cause of burnout in nurses working in private centers includes lack of a significant relation between the income and wages and its difficulty. One-sample T-test results in this study also demonstrated that "low wages" has the highest priority of burnout in the workplace from the perspective of nurses in University of Medical Sciences of Shahrood. Therefore, results of these two studies are consistent with each other. In addition, research results by Azizinejad and Hosseini (2006) also showed that the most frequent causes of burnout include low salary, lack of social support, and lack of management support, job insecurity, and long working hours. The results also showed that the highest causes of burnout among nurses in University of Medical Sciences of Shahrood includes low wages, a sense of injustice in payments, disregarding the dignity and status of nurses, working hours, high workloads. The results of these two studies are very close to each other. As noted, the factors influencing burnout among nurses in University of Medical Sciences include: job stress, job dissatisfaction, physical environment, burnout and economic factors. In addition, the opinions of the respondents are the same on the factors affecting occupational burnout in terms of age, marital status, work experience and work shift. Therefore, the final analysis model can be considered as follows. Page 1409

19 Graph 4. Final analysis model Components Job stress Job dissatisfaction Physical environment Burnout Economic factor Mediating variable Age Marital status Work experience Working shift Concept Effecting factors in burnout among nurses in University of Medical Sciences of Shahrood Page 1410

20 References 1. Aghajani, M. (2012), comparison of burnout among nurses in different parts of Nursing, Journal of Research Development in Nursing & Midwifery, Volume IX, Issue Two, pp Bowsari, M. (2002), burnout and related factors among female nurses working in hospitals of Zanjan, , Journal of Zanjan University of Medical Sciences and Health Services, No. 40, pp Delpasand, M., Raeesi, P.; Bagdeli, F; Shahabi, M. (2010), the impact of job rotation on burnout among nurses in Kashani hospital in Tehran: A Case Study, Iran Occupational Health Journal, Volume 7, Issue 4, Pages Rafie, F; Shamsikhani, S., Zarei, M., Haqqani, H.; Shamsikhani, S. (2012), the relationship between burnout and individual characteristics of nurses, Nursing Care Research Center, Tehran University of Medical Sciences (College of Nursing), Volume 25, Issue 78, pp Embriaco N, Papazian L, Kentish-Barnes N, Pochard F, Azoulag E. (2007), Burnout syndrome among critical carehealth care workers, Curr Opin Crit care; 13(5): Atef, L., Rouholamini, M., Noori, A. and Molavi, H. (2006), burnout and job satisfaction among general surgeons and experts in Isfahan, Journal of research and scholarship in Psychology, Islamic Azad University Branch (Isfahan), Number 29, year 8. Page 1411