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(R) 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.
