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Felix Ladstätter/Eva Garrosa Prediction of Burnout An Artificial Neural Network Approach Diplom.de Felix Ladstätter/Eva Garrosa Prediction of Burnout An Artificial Neural Network Approach ISBN: 978-3-8366-1141-1 Druck Diplomica® Verlag GmbH, Hamburg, 2008 Dieses Werk ist urheberrechtlich geschützt. Die dadurch begründeten Rechte, insbesondere die der Übersetzung, des Nachdrucks, des Vortrags, der Entnahme von Abbildungen und Tabellen, der Funksendung, der Mikroverfilmung oder der Vervielfältigung auf anderen Wegen und der Speicherung in Datenverarbeitungsanlagen, bleiben, auch bei nur auszugsweiser Verwertung, vorbehalten. Eine Vervielfältigung dieses Werkes oder von Teilen dieses Werkes ist auch im Einzelfall nur in den Grenzen der gesetzlichen Bestimmungen des Urheberrechtsgesetzes der Bundesrepublik Deutschland in der jeweils geltenden Fassung zulässig. Sie ist grundsätzlich vergütungspflichtig. Zuwiderhandlungen unterliegen den Strafbestimmungen des Urheberrechtes. Die Wiedergabe von Gebrauchsnamen, Handelsnamen, Warenbezeichnungen usw. in diesem Werk berechtigt auch ohne besondere Kennzeichnung nicht zu der Annahme, dass solche Namen im Sinne der Warenzeichen- und Markenschutz-Gesetzgebung als frei zu betrachten wären und daher von jedermann benutzt werden dürften. Die Informationen in diesem Werk wurden mit Sorgfalt erarbeitet. Dennoch können Fehler nicht vollständig ausgeschlossen werden und der Verlag, die Autoren oder Übersetzer übernehmen keine juristische Verantwortung oder irgendeine Haftung für evtl. verbliebene fehlerhafte Angaben und deren Folgen. © Diplomica Verlag GmbH http://www.diplomica.de, Hamburg 2008 Printed in Germany Contents Contents.............................................................................................i List of Figures..................................................................................iv List of Tables..................................................................................vii 1 Burnout......................................................................................... 1 1.1 The Origin of Burnout............................................................................1 1.1.1 The Uncovering of Burnout........................................................2 1.2 Burnout as a Global Problem.................................................................3 1.3 Why is Burnout increasing?...................................................................4 1.4 Understanding Burnout..........................................................................7 1.4.1 Definitions..................................................................................8 1.4.2 Possible Symptoms...................................................................10 1.4.3 Burnout vs. Job Stress...............................................................13 1.4.4 Burnout vs. Depression.............................................................14 1.4.5 Burnout vs. Chronic Fatigue.....................................................14 1.5 Assessment and Prevalence..................................................................15 1.5.1 Assessment Tools.....................................................................15 1.5.2 Reliability and Validity.............................................................16 1.5.3 Self-report Measures of Burnout..............................................18 1.5.4 How often does Burnout occur?...............................................21 1.6 Correlates, Causes and Consequences..................................................22 1.6.1 Possible Antecedents of Burnout..............................................24 1.6.2 Possible Consequences of Burnout...........................................28 1.7 Theoretical Approaches to Explain Burnout........................................30 1.7.1 An Integrative Model................................................................31 i 1.8 Prevention and Intervention of Burnout...............................................33 1.8.1 Classification............................................................................33 1.8.2 Individual Level Interventions..................................................35 1.8.3 Individual/Organizational Level Interventions.........................38 1.8.4 Organizational Level Interventions..........................................42 2 Artificial Neural Networks..........................................................47 2.1 Introduction to Neurocomputing..........................................................47 2.1.1 Biological Motivation...............................................................48 2.1.2 Evolution of Artificial Neural Networks..................................50 2.1.3 Categorization of Artificial Neural Networks..........................52 2.2 Artificial Neuron Model.......................................................................53 2.2.1 Notation and Terminology.......................................................53 2.2.2 Single-Input Neuron.................................................................54 2.3 Basic Transfer Functions......................................................................55 2.3.1 Hard Limit Transfer Function..................................................56 2.3.2 Linear Transfer Function..........................................................57 2.3.3 Sigmoid Transfer Function.......................................................57 2.3.4 Hyperbolic Tangent Sigmoid Transfer Function......................58 2.3.5 Radial Basis Transfer Function (Gaussian Function)...............59 2.4 Multiple-Input Neuron.........................................................................60 2.5 Training Algorithms.............................................................................61 2.6 Network Architectures.........................................................................63 2.6.1 A Single Layer of Neurons.......................................................63 2.6.2 Multiple Layers of Neurons......................................................64 2.7 Perceptron.............................................................................................66 2.7.1 Perceptron Learning Rule.........................................................68 2.7.2 The Perceptron Training Algorithm.........................................69 2.7.3 Limitations of the Perceptron...................................................70 2.8 Self-Organizing Map (SOM)...............................................................71 2.8.1 Competitive Learning...............................................................72 2.8.2 Kohonen Training Algorithm...................................................78 2.8.3 Example of the Kohonen Algorithm........................................79 2.8.4 Problems with the Kohonen Algorithm....................................80 2.9 Multi-layer Feed-forward Networks....................................................82 2.9.1 Hidden-Neurons.......................................................................84 2.9.2 Back-propagation.....................................................................85 2.9.3 Back-propagation Training Algorithm.....................................91 2.9.4 Problems with Back-propagation.............................................99 ii 2.10 Radial Basis Function (RBF) Network...............................................107 2.10.1 Functioning of the Radial Basis Network...............................111 2.10.2 The Pseudo Inverse (PI) RBF Training Algorithm.................113 2.10.3 Example of the PI RBF Algorithm.........................................116 2.10.4 The Hybrid RBF Training Algorithm.....................................118 2.10.5 Example of the Hybrid RBF Training Algorithm...................124 2.10.6 Problems with Radial Basis Function Networks....................128 3 Application of ANNs to Burnout Data.....................................130 3.1 Introduction........................................................................................131 3.1.1 The Nursing Profession..........................................................131 3.1.2 Burnout in Nurses...................................................................132 3.1.3 Objective.................................................................................135 3.2 Data....................................................................................................136 3.2.1 Participants.............................................................................136 3.2.2 Measures.................................................................................137 3.2.3 Statistical Data Analysis.........................................................138 3.2.4 Variables used for the Development of the ANNs.................138 3.3 Implementation of the NuBuNet (Nursing Burnout Network)...........139 3.3.1 Self-Organizing Map (SOM)..................................................140 3.3.2 Three-layer Feed-forward Back-propagation Network..........142 3.3.3 Radial Basis Function Network..............................................144 3.4 Processing the Data............................................................................145 3.4.1 Data Preparation (Pre-Processing)..........................................145 3.4.2 Network Preparation and Training.........................................148 3.4.3 Post-Processing.......................................................................152 3.5 Results................................................................................................152 3.5.1 Three-layer Feed-forward Back-propagation Network..........153 3.5.2 Radial Basis Function Network (PI Algorithm).....................168 3.5.3 Radial Basis Function Network (Hybrid Algorithm).............179 3.5.4 Comparison of the Results......................................................197 3.6 Discussion..........................................................................................199 4 References ................................................................................207 4.1 Burnout...............................................................................................207 4.1.1 Internet Directions..................................................................218 4.2 Artificial Neural Networks.................................................................219 4.2.1 Internet Directions..................................................................225 iii List of Figures Figure 1.1 Stress curve (A, B) vs. burnout curve(C, D) (adapted from Brill (1984)).....................................................................................................13 Figure 1.2 Integrative model of burnout (adapted from Schaufeli and Enzmann (1998)).............................................................................................32 Figure 1.3 Cognitive behavioral chain............................................................37 Figure 2.1 Illustration of biological neurons (adapted from Hagan et al., 1996)................................................................................................................49 Figure 2.2 Sum of signals from three neurons.................................................50 Figure 2.3 Single-input neuron........................................................................55 Figure 2.4 Hard limit transfer function............................................................56 Figure 2.5 Linear transfer function..................................................................57 Figure 2.6 Sigmoid transfer function...............................................................58 Figure 2.7 Hyperbolic tangent sigmoid transfer function...............................59 Figure 2.8 Radial basis transfer function.........................................................60 Figure 2.9 Multiple-input neuron....................................................................60 Figure 2.10 A single layer of neurons.............................................................63 Figure 2.11 Three-layer network.....................................................................65 Figure 2.12 Perceptron with N inputs..............................................................67 Figure 2.13 Linear separability in a two-input perceptron..............................68 Figure 2.14 Two-dimensional plots of three logical operations......................70 Figure 2.15 Kohonen SOM (adapted from Negnevitsky (2005))....................73 iv Figure 2.16 Kohonen network........................................................................74 Figure 2.17 Euclidean distance between input vector p and weight vector w..........................................................................................................75 j Figure 2.18 Mexican hat function...................................................................76 Figure 2.19 Graphical representation of a moving winner neuron.................77 Figure 2.20 Rectangular Neighborhood function...........................................77 Figure 2.21 Multi-layer feed-forward neural network....................................82 Figure 2.22 Input-layer neuron.......................................................................83 Figure 2.23 Output-layer neuron....................................................................84 Figure 2.24 Hidden-layer neuron....................................................................85 Figure 2.25 Weight and bias of neurons.........................................................87 Figure 2.26 Three-layer feed-forward back-propagation network.................92 Figure 2.27 Example 1-2-1 network...............................................................96 Figure 2.28 Two-dimensional example of a local and a global minimum...100 Figure 2.29 Example function used to investigate local and global minima 101 Figure 2.30 Mean squared error surface varying θ1 and w1 ......................102 2 2,1 Figure 2.31 Mean squared error surface varyingw2 and w1 .....................103 1,2 2,1 Figure 2.32 Momentum effect on oscillation...............................................105 Figure 2.33 Radial basis function network...................................................108 Figure 2.34 Radial basis neuron...................................................................109 Figure 3.1 Factors affecting nursing burnout (adapted from Garrosa et al. (2008))..........................................................................................................139 Figure 3.2 Architecture of the SOM network for the data pre-processing...141 Figure 3.3 Architecture of the three-layer feed-forward network used for the burnout-model approximation...........................................................143 Figure 3.4 Architecture of the RBF network used for the burnout-model approximation...............................................................................................144 Figure 3.5 Illustration of the burnout data pre-processing steps..................147 Figure 3.6 MLP training results....................................................................160 Figure 3.7 MLP validation results................................................................161 v Figure 3.8 Linear regression for the emotional exhaustion dim....................162 Figure 3.9 Linear regression for the depersonalization dim..........................162 Figure 3.10 Linear regression for the lack of pers. accomplishment dim.....163 Figure 3.11 Confidence interval for the emotional exhaustion dim..............164 Figure 3.12 Confidence interval for the depersonalization dim....................165 Figure 3.13 Confidence interval for the lack of pers. accomplishment dim.166 Figure 3.14 RBFPINet training results..........................................................171 Figure 3.15 RBFPINet validation results......................................................172 Figure 3.16 Linear regression for the emotional exhaustion dim..................173 Figure 3.17 Linear regression for the depersonalization dim........................174 Figure 3.18 Linear regression for the lack of pers. accomplishment dim.....174 Figure 3.19 Confidence interval for the emotional exhaustion dim..............175 Figure 3.20 Confidence interval for the depersonalization dim....................177 Figure 3.21 Confidence interval for the lack of pers. accomplishment dim.178 Figure 3.22 RBFHNet training results...........................................................189 Figure 3.23 RBFHNet validation results.......................................................190 Figure 3.24 Linear regression for the emotional exhaustion dim..................191 Figure 3.25 Linear regression for the depersonalization dim........................192 Figure 3.26 Linear regression for the lack of pers. accomplishment dim.....192 Figure 3.27 Confidence interval for the emotional exhaustion dim..............193 Figure 3.28 Confidence interval for the depersonalization dim....................194 Figure 3.29 Confidence interval for the lack of pers. accomplishment dim.195 vi List of Tables Table 1.1 Possible burnout symptoms at individual level (adapted from Schaufeli and Enzmann (1998)).....................................................................11 Table 1.2 Normative data of the MBI based on 73 US studies published between 1979 and 1998 (adapted from Schaufeli and Enzmann (1998)).......22 Table 1.3 Possible causes of burnout (adapted from Schaufeli and Enzmann (1998))............................................................................................23 Table 1.4 Possible consequences of burnout (adapted from Schaufeli and Enzmann (1998))............................................................................................28 Table 1.5 Overview of burnout interventions (adapted from Schaufeli and Enzmann (1998))............................................................................................34 Table 3.1 Specific characteristics of the nursing profession........................133 Table 3.2 Data sets for network training and validation after pre-processing...............................................................................................148 Table 3.3 MLP parameter for the burnout-model network...........................149 Table 3.4 RBF (PI algorithm) parameter for the burnout-model network...150 Table 3.5 RBF (hybrid algorithm) parameter for the burnout-model network.........................................................................................................151 Table 3.6 Descriptive statistic of the burnout-model data............................152 Table 3.7 All MLP results for data set 1.......................................................154 Table 3.8 All MLP results for data set 2.......................................................155 Table 3.9 All MLP results for data set 3.......................................................156 vii Table 3.10 Means of the MLP results for data set 1......................................157 Table 3.11 Means of the MLP results for data set 2......................................157 Table 3.12 Means of the MLP results for data set 3......................................158 Table 3.13 Means of the MLP results for all data sets..................................158 Table 3.14 Means of the MLP results for all numbers of neurons................159 Table 3.15 Examples for the prediction of the emotional exhaustion dim....164 Table 3.16 Examples for the prediction of the depersonalization dim..........165 Table 3.17 Examples for the prediction of the lack of pers. accompl. dim...167 Table 3.18 Hidden-layer weights...................................................................167 Table 3.19 Hidden-layer biases.....................................................................168 Table 3.20 Output-layer weights...................................................................168 Table 3.21 Output-layer biases......................................................................168 Table 3.22 All RBFPINet results for data set 1.............................................169 Table 3.23 All RBFPINet results for data set 2.............................................170 Table 3.24 All RBFPINet results for data set 3.............................................170 Table 3.25 Examples for the prediction of the emotional exhaustion dim....176 Table 3.26 Examples for the prediction of the depersonalization dim..........177 Table 3.27 Examples for the prediction of the lack of pers. accompl. dim...178 Table 3.28 RBFHNet with σ = 0.54, α = 0.01, β = 0.00001, γ = 0................180 Table 3.29 RBFHNet with σ = 0.54, α = 0.01, β = γ = 0.00001....................180 Table 3.30 RBFHNet with σ = 0.54, α = 0.01, β = γ = 0.00005....................180 Table 3.31 RBFHNet with σ = 0.54, α = 0.01, β = 0, γ = 0.00001................181 Table 3.32 RBFHNet with σ = 0.54, α = 0.02, β = 0.00001, γ = 0................181 Table 3.33 RBFHNet with σ = 0.54, α = 0.02, β = γ = 0.00001....................182 Table 3.34 RBFHNet with σ = 0.54, α = 0.02, β = γ = 0.00005....................182 Table 3.35 RBFHNet with σ = 0.54, α = 0.02, β = 0.00001, γ = 0................182 Table 3.36 RBFHNet means of Table 3.28 to Table 3.35.............................183 Table 3.37 RBFHNet with σ = 1.10, α = 0.01, β = 0.00001, γ = 0................184 Table 3.38 RBFHNet with σ = 1.10, α = 0.01, β = γ = 0.00001...................184 Table 3.39 RBFHNet with σ = 1.10, α = 0.01, β = γ = 0.00005...................185 Table 3.40 RBFHNet with σ = 1.10, α = 0.01, β = 0, γ = 0.00001................185 viii

Prediction of burnout : an artificial neural network approach PDF

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by Garrosa, Eva, Ladstätter, Felix

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Author:	Garrosa, Eva; Ladstätter, Felix
Publication Year:	2008
ISBN:	3836611414
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