This Study about burnout in nurses takes a different approach than all prior empirical work on this topic given that nonlinear relationships between job stressors, personal factors and the three burnout dimensions are investigated using artificial neural networks, a type of computer simulation that is especially well suited to capturing nonlinearities in data. The burnout process is related to organizational, personal, interpersonal, social, and cultural variables and these relationships are not exclusively linear. Due to this nonlinearity, hierarchical stepwise multiple regression or other linear statistical methods, may perhaps not be the most suitable method to analyze the data effectively. Compounding the dilemma is that multiple linear regression returns no direct indicator with regard to whether the data is best portrayed linearly. In standard least squares linear regression, the model has to be specified previously and assumptions have to be made concerning the underlying relationship between independent variables and dependent variables. Since by default, the relationship is often assumed to be linear, the regression line can be erroneous even though the error of the fit can be small. Artificial neural networks do not have these limitations with nonlinearities and are therefore predestined for the analysis of nonlinear relationships. This study is a complex research of burnout that includes socio-demographic characteristics, job stressors, and hardy personality. Typically, studies on burnout have investigated primarily the effects of organizational factors. Recently, authors revealed and confirmed the important effects of personality variables on the burnout process. The objective of developing an instrument to predict burnout (NuBuNet abbreviation for Nursing Burnout Network) in nurses is accomplished by using two different types of artificial neural networks: A three-layer feed-forward network with the gradient decent back-propagation training algorithm and a radial basis function network with two different training algorithms: the pseudo inverse algorithm and a hybrid algorithm. The implementation of the artificial neural networks used in this study is carried out in a MATLAB® development environment. Instead of writing each artificial neural network as a stand-alone program, an object-oriented programming style is chosen to include all functions into one single system. Three artificial neural networks are implemented in the technical part of this study. A self-organizing map, a three-layer back-propagation network, and a radial basis function network. Whereas the self-organizing map is only used in the data preparation process, the back-propagation network and the radial basis function network is used in the burnout model approximation. After an exhaustive training and simulation session including more than 150 networks and an analysis of all results, the network with the best results is chosen to be compared to the hierarchical stepwise multiple regression. The network paradigms are better suited for the analysis of burnout than hierarchical stepwise multiple regression. Both can capture nonlinear relationships that are relevant for theory development. At predicting the three burnout sub-dimensions emotional exhaustion, depersonalization, and lack of personal accomplishment however, the radial basis function network is slightly better than the three-layer feed-forward network.

Text Sample: Possible Antecedents of Burnout: Possible causes of burnout can be classified into personality variables, work-related attitudes, and work and organizational variables. Besides above mentioned variables, Table 1.3 exhibits socio-demographic variables, even if they are no causes of burnout. However these characteristics may be linked to other factors, like gender to role taking, role expectations, or ‘feeling type’. Similarly, age is not a cause of burnout but it may be related to age-dependent factors like occupational socialization. The number of minus or plus signs in Table 1.3 on page 23 indicates the strength and the direction of the correlation with burnout, founded on three subjective criterions: (1) the number of studies that found clear evidence for the relationship (2) the methodological quality of these studies (3) the consistency of the results across studies. Socio-demographic variables: The most consistently to burnout connected socio-demographic variable is the age (Maslach, Schaufeli, & Leiter, 2001) Younger employees experience a higher burnout rate than those aged over 30 or 40 years or in other words, it seems that burnout takes place rather at the beginning of the career. This confirms the observation that burnout is negatively related to work experience. Some authors interpret the higher rate of burnout among the younger and less experienced persons as a reality shock. The other biographical characteristics do not show such clear relationships with burnout, although there are some studies showing that burnout takes place more frequently amongst woman than men. One explanation may be that, as a result of additional responsibilities at home, working woman experience higher overall workloads compared with working men, and workload is in turn positively related to burnout. Personality variables: It is somewhat difficult to interpret the meaning of correlations of burnout with personality features since persons interact with situations in complex ways. Even a high relationship of a particular personality characteristic does not necessarily involve causality. However, there are many studies which show that a ‘hardy personality’, characterized by participation in daily activities, a feeling of control over events, and openness to change, is consistently related to all three dimensions of the MBI. In other words, the more hardy a person is, the less burned-out he or she will be (Maslach et al., 2001). Another strong related personal characteristic is neuroticism, which includes trait anxiety, hostility, depression, self-consciousness and vulnerability. A neurotic person is emotionally unstable and she or he seems to be predisposed to experience burnout (Schaufeli & Enzmann, 1998). A person’s control orientation may either be external or internal. Individuals with an external control orientation attribute events and achievements to powerful others or to chance, whereas those with an internal control orientation ascribe events and achievements to their own effort, ability, and willingness to risk. External control orientated persons are, compared with internal control orientated persons, more emotionally exhausted, depersonalized, and experience feelings of personal accomplishment (Glass & McKnight, 1996). Another interesting and important relationship was found between a person’s coping style and burnout. Those individuals who are burned out cope with stressful events in a rather passive, defensive way, whereas an active and confronting coping style is used by less burned out persons. Work and organizational variables: Workload and time pressure are highly related to emotional exhaustion but, and this is striking, practically unrelated to personal accomplishment. Role stress, role conflict, and role ambiguity correlate fairly to substantially with burnout. Role theory (e.g., Jackson & Schuler, 1985 Katz & Kahn, 1978) suggests that inter-role conflict and tension often results in individuals who find it increasingly difficult to successfully execute each of their roles because of constrained resources (e.g., energy, time) or the incompatibility among different roles (e.g., employee roles vs. family roles). Specifically, role stress emerges from the impact of the environment on an individual’s ability to fulfill role expectations (Beehr & Glazer, 2005). During the past decades, the number of (especially female) individuals having two or more jobs (due to economical reasons) has increased steadily. In the light of role theory, this development which implicates role stress since these individuals have to fulfill two or, when the family role is included three roles, is associated with negative consequences for the individuals and the organizations.

• Informatik

• Netzwerk

• Neural Network

• Regression

• Radial Basis Function Network