1 - Prediction of Burnout An Artificial Neural Network Approach [Seite 1]
1.1 - Contents [Seite 3]
1.2 - List of Figures [Seite 6]
1.3 - List of Tables [Seite 9]
1.4 - 1 Burnout [Seite 12]
1.4.1 - 1.1 The Origin of Burnout [Seite 12]
1.4.1.1 - 1.1.1 The Uncovering of Burnout [Seite 13]
1.4.2 - 1.2 Burnout as a Global Problem [Seite 14]
1.4.3 - 1.3 Why is Burnout increasing? [Seite 15]
1.4.4 - 1.4 Understanding Burnout [Seite 18]
1.4.4.1 - 1.4.1 Definitions [Seite 19]
1.4.4.2 - 1.4.2 Possible Symptoms [Seite 21]
1.4.4.3 - 1.4.3 Burnout vs. Job Stress [Seite 24]
1.4.4.4 - 1.4.4 Burnout vs. Depression [Seite 25]
1.4.4.5 - 1.4.5 Burnout vs. Chronic Fatigue [Seite 25]
1.4.5 - 1.5 Assessment and Prevalence [Seite 26]
1.4.5.1 - 1.5.1 Assessment Tools [Seite 26]
1.4.5.2 - 1.5.2 Reliability and Validity [Seite 27]
1.4.5.3 - 1.5.3 Self-report Measures of Burnout [Seite 29]
1.4.5.4 - 1.5.4 How often does Burnout occur? [Seite 32]
1.4.6 - 1.6 Correlates, Causes and Consequences [Seite 33]
1.4.6.1 - 1.6.1 Possible Antecedents of Burnout [Seite 35]
1.4.6.2 - 1.6.2 Possible Consequences of Burnout [Seite 39]
1.4.7 - 1.7 Theoretical Approaches to Explain Burnout [Seite 41]
1.4.7.1 - 1.7.1 An Integrative Model [Seite 42]
1.4.8 - 1.8 Prevention and Intervention of Burnout [Seite 44]
1.4.8.1 - 1.8.1 Classification [Seite 44]
1.4.8.2 - 1.8.2 Individual Level Interventions [Seite 46]
1.4.8.3 - 1.8.3 Individual/Organizational Level Interventions [Seite 49]
1.4.9 - 1.8.4 Organizational Level Interventions [Seite 53]
1.5 - 2 Artificial Neural Networks [Seite 57]
1.5.1 - 2.1 Introduction to Neurocomputing [Seite 57]
1.5.1.1 - 2.1.1 Biological Motivation [Seite 58]
1.5.1.2 - 2.1.2 Evolution of Artificial Neural Networks [Seite 60]
1.5.1.3 - 2.1.3 Categorization of Artificial Neural Networks [Seite 62]
1.5.2 - 2.2 Artificial Neuron Model [Seite 63]
1.5.2.1 - 2.2.1 Notation and Terminology [Seite 63]
1.5.2.2 - 2.2.2 Single-Input Neuron [Seite 64]
1.5.3 - 2.3 Basic Transfer Functions [Seite 65]
1.5.3.1 - 2.3.1 Hard Limit Transfer Function [Seite 66]
1.5.3.2 - 2.3.2 Linear Transfer Function [Seite 67]
1.5.3.3 - 2.3.3 Sigmoid Transfer Function [Seite 67]
1.5.3.4 - 2.3.4 Hyperbolic Tangent Sigmoid Transfer Function [Seite 68]
1.5.3.5 - 2.3.5 Radial Basis Transfer Function (GaussianFunction) [Seite 69]
1.5.4 - 2.4 Multiple-Input Neuron [Seite 70]
1.5.5 - 2.5 Training Algorithms [Seite 71]
1.5.6 - 2.6 Network Architectures [Seite 73]
1.5.7 - 2.6.1 A Single Layer of Neurons [Seite 73]
1.5.8 - 2.6.2 Multiple Layers of Neurons [Seite 74]
1.5.9 - 2.7 Perceptron [Seite 76]
1.5.9.1 - 2.7.1 Perceptron Learning Rule [Seite 78]
1.5.9.2 - 2.7.2 The Perceptron Training Algorithm [Seite 79]
1.5.10 - 2.7.3 Limitations of the Perceptron [Seite 80]
1.5.11 - 2.8 Self-Organizing Map (SOM) [Seite 81]
1.5.11.1 - 2.8.1 Competitive Learning [Seite 82]
1.5.11.2 - 2.8.2 Kohonen Training Algorithm [Seite 88]
1.5.11.3 - 2.8.3 Example of the Kohonen Algorithm [Seite 89]
1.5.11.4 - 2.8.4 Problems with the Kohonen Algorithm [Seite 90]
1.5.12 - 2.9 Multi-layer Feed-forward Networks [Seite 92]
1.5.12.1 - 2.9.1 Hidden-Neurons [Seite 94]
1.5.12.2 - 2.9.2 Back-propagation [Seite 95]
1.5.12.3 - 2.9.3 Back-propagation Training Algorithm [Seite 101]
1.5.12.4 - 2.9.4 Problems with Back-propagation [Seite 109]
1.5.13 - 2.10 Radial Basis Function (RBF) Network [Seite 117]
1.5.13.1 - 2.10.1 Functioning of the Radial Basis Network [Seite 121]
1.5.13.2 - 2.10.2 The Pseudo Inverse (PI) RBF TrainingAlgorithm [Seite 123]
1.5.13.3 - 2.10.3 Example of the PI RBF Algorithm [Seite 126]
1.5.13.4 - 2.10.4 The Hybrid RBF Training Algorithm [Seite 128]
1.5.13.5 - 2.10.5 Example of the Hybrid RBF Training Algorithm [Seite 134]
1.5.13.6 - 2.10.6 Problems with Radial Basis Function Networks [Seite 138]
1.6 - 3 Application of ANNs toBurnout Data [Seite 140]
1.6.1 - 3.1 Introduction [Seite 141]
1.6.1.1 - 3.1.1 The Nursing Profession [Seite 141]
1.6.1.2 - 3.1.2 Burnout in Nurses [Seite 142]
1.6.1.3 - 3.1.3 Objective [Seite 145]
1.6.2 - 3.2 Data [Seite 146]
1.6.2.1 - 3.2.1 Participants [Seite 146]
1.6.2.2 - 3.2.2 Measures [Seite 147]
1.6.2.3 - 3.2.3 Statistical Data Analysis [Seite 148]
1.6.2.4 - 3.2.4 Variables used for the Development of the ANNs [Seite 148]
1.6.3 - 3.3 Implementation of the NuBuNet (NursingBurnout Network) [Seite 149]
1.6.3.1 - 3.3.1 Self-Organizing Map (SOM) [Seite 150]
1.6.3.2 - 3.3.2 Three-layer Feed-forward Back-propagationNetwork [Seite 152]
1.6.3.3 - 3.3.3 Radial Basis Function Network [Seite 154]
1.6.4 - 3.4 Processing the Data [Seite 155]
1.6.4.1 - 3.4.1 Data Preparation (Pre-Processing) [Seite 155]
1.6.4.2 - 3.4.2 Network Preparation and Training [Seite 158]
1.6.4.3 - 3.4.3 Post-Processing [Seite 162]
1.6.5 - 3.5 Results [Seite 162]
1.6.5.1 - 3.5.1 Three-layer Feed-forward Back-propagationNetwork [Seite 163]
1.6.5.2 - 3.5.2 Radial Basis Function Network (PI Algorithm) [Seite 178]
1.6.5.3 - 3.5.3 Radial Basis Function Network (HybridAlgorithm) [Seite 189]
1.6.5.4 - 3.5.4 Comparison of the Results [Seite 207]
1.6.6 - 3.6 Discussion [Seite 209]
1.7 - 4 References [Seite 217]
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.
