Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo–based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Practitioner Summary: Many relationships among variables (e.g. stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

[1]  V. Lambert,et al.  Workplace and personal factors associated with physical and mental health in hospital nurses in China. , 2007, Nursing & health sciences.

[2]  P. Hanges,et al.  Integrating Neural Networks into Decision-Making and Motivational Theory: Rethinking VIE Theory * , 2003 .

[3]  Alfred DeMaris,et al.  Regression With Social Data: Modeling Continuous and Limited Response Variables , 2004 .

[4]  F. Ducharme,et al.  Psychosocial determinants of burnout in geriatric nursing. , 1995, International journal of nursing studies.

[5]  M. Bertini,et al.  Job characteristics, organizational conditions, and distress/well-being among Italian and Dutch nurses: a cross-national comparison. , 2011, International journal of nursing studies.

[6]  E. Garrosa,et al.  Neural Network Analysis of Nonlinear Effects of Hardiness on Burnout in Chinese Nurses , 2014 .

[7]  M. Gordon,et al.  The relationship of burnout, stress, and hardiness in nurses in a military medical center: a replicated descriptive study. , 1999, The Journal of burn care & rehabilitation.

[8]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models , 2004 .

[9]  A. Adler,et al.  Military hardiness as a buffer of psychological health on return from deployment. , 2006, Military medicine.

[10]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[11]  T.,et al.  Training Feedforward Networks with the Marquardt Algorithm , 2004 .

[12]  Michael Negnevitsky,et al.  Artificial Intelligence: A Guide to Intelligent Systems , 2001 .

[13]  Martin T. Hagan,et al.  Neural network design , 1995 .

[14]  J. Lim,et al.  Stress and coping in Australian nurses: a systematic review. , 2010, International nursing review.

[15]  S. Jackson,et al.  Maslach burnout inventory manual , 1996 .

[16]  L. Cranley,et al.  Nurse intention to remain employed: understanding and strengthening determinants. , 2006, Journal of advanced nursing.

[17]  Dick Schoech,et al.  Building a Model to Predict Caseworker and Supervisor Turnover Using a Neural Network and Logistic Regression , 2002 .

[18]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[19]  V. Lambert,et al.  Predictors of physical and mental health in hospital nurses within the People's Republic of China. , 2007, International nursing review.

[20]  T. Willemsen,et al.  Determinants of general fatigue and emotional exhaustion: a prospective study , 2004 .

[21]  G. David Garson,et al.  Neural Networks: An Introductory Guide for Social Scientists , 1999 .

[22]  Blake E. Ashforth,et al.  A meta-analytic examination of the correlates of the three dimensions of job burnout. , 1996, The Journal of applied psychology.

[23]  Sang Min Lee,et al.  Antecedents and consequences of three dimensions of burnout in psychotherapists: A meta-analysis. , 2011 .

[24]  Shixue Li,et al.  Sharps injuries and job burnout: a cross-sectional study among nurses in China. , 2012, Nursing & health sciences.

[25]  Brian W. Swider,et al.  Born to burnout: A meta-analytic path model of personality, job burnout, and work outcomes , 2010 .

[26]  Mark John Somers,et al.  Neural Networks in Organizational Research: Applying Pattern Recognition to the Analysis of Organizational Behavior , 2006 .

[27]  E. White,et al.  Stress, coping, burnout and job satisfaction in British nurses: findings from the Clinical Supervision Evaluation Project , 1999 .

[28]  Sigurd W. Hystad,et al.  Academic Stress and Health: Exploring the Moderating Role of Personality Hardiness , 2009 .

[29]  Susan W. Palocsay,et al.  Neural Network Modeling in Cross-Cultural Research: A Comparison with Multiple Regression , 2004 .

[30]  Salvatore R. Maddi,et al.  Hardiness: The courage to grow from stresses , 2006 .

[31]  Fernando Gustavo Tomasel,et al.  Prediction of functional characteristics of ecosystems: a comparison of artificial neural networks and regression models , 1997 .

[32]  Larry T. Looper,et al.  Neural Networks: A Primer , 1991 .

[33]  Judith M. Collins,et al.  AN APPLICATION OF THE THEORY OF NEURAL COMPUTATION TO THE PREDICTION OF WORKPLACE BEHAVIOR: AN ILLUSTRATION AND ASSESSMENT OF NETWORK ANALYSIS , 1993 .

[34]  Martin T. Hagan,et al.  Gauss-Newton approximation to Bayesian learning , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[35]  Gene M. Alarcon,et al.  A meta-analytic examination of hardiness. , 2010 .

[36]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[37]  Steven C. Wheelwright,et al.  Forecasting: Methods and Applications, 3rd Ed , 1997 .

[38]  A. Rodríguez-Muñoz,et al.  Role stress and personal resources in nursing: a cross-sectional study of burnout and engagement. , 2011, International journal of nursing studies.

[39]  Mark John Somers,et al.  Thinking differently: Assessing nonlinearities in the relationship between work attitudes and job performance using a Bayesian neural network , 2001 .

[40]  Mark John Somers,et al.  Using Artificial Neural Networks to Model Nonlinearity , 2009 .

[41]  T. Reilly,et al.  Relationships between leisure-time energy expenditure and individual coping strategies for shift-work , 2009, Ergonomics.

[42]  D. Zapf,et al.  Emotion work and job stressors and their effects on burnout , 2001, Psychology & health.

[43]  L. Aiken,et al.  Relationship between Psychiatric Nurse Work Environments and Nurse Burnout in Acute Care General Hospitals , 2010, Issues in mental health nursing.

[44]  M. Somers,et al.  Can a Model of the Brain Inform Research and Practice in Organizations , 2006 .

[45]  B. Arnetz,et al.  Work environment, skills utilization and health of Swedish nurses--results from a national questionnaire study. , 1995, Psychotherapy and psychosomatics.

[46]  Bo Chen,et al.  Nurse burnout and its association with occupational stress in a cross-sectional study in Shanghai. , 2011, Journal of advanced nursing.

[47]  W. Schaufeli,et al.  Do burnout and work engagement predict depressive symptoms and life satisfaction? A three-wave seven-year prospective study. , 2012, Journal of affective disorders.

[48]  H. Laschinger,et al.  The influence of personal dispositional factors and organizational resources on workplace violence, burnout, and health outcomes in new graduate nurses: a cross-sectional study. , 2012, International journal of nursing studies.

[49]  Tom Cox,et al.  The use of artificial neural networks and multiple linear regression in modelling work–health relationships: Translating theory into analytical practice , 2010 .

[50]  Arjen van Witteloostuijn,et al.  From the Editors: Common method variance in international business research , 2010 .

[51]  Kristen Bell DeTienne,et al.  Neural Networks as Statistical Tools for Business Researchers , 2003 .

[52]  Michael D. McKay,et al.  Evaluating Prediction Uncertainty , 1995 .

[53]  R. Iman,et al.  A distribution-free approach to inducing rank correlation among input variables , 1982 .

[54]  White Paper on the Nature and Scope of Issues on Adoption of Model Use Acceptability Guidance , 1999 .

[55]  B. Moreno-Jiménez,et al.  The relationship between socio-demographic variables, job stressors, burnout, and hardy personality in nurses: an exploratory study. , 2008, International journal of nursing studies.

[56]  M. Gevrey,et al.  Review and comparison of methods to study the contribution of variables in artificial neural network models , 2003 .

[57]  M. Somers,et al.  Application of two neural network paradigms to the study of voluntary employee turnover. , 1999, The Journal of applied psychology.

[58]  Lennart Ljung,et al.  Nonlinear black-box modeling in system identification: a unified overview , 1995, Autom..

[59]  F. Psicología,et al.  El Desgaste profesional de enfermera. Desarrollo y validacin factorial del CDPE , 2000 .

[60]  E. Garrosa,et al.  Application of artificial neural networks to a study of nursing burnout , 2010, Ergonomics.

[61]  C. Hsieh,et al.  [The relationship between hardiness, coping strategies and burnout in psychiatric nurses]. , 2004, Hu li za zhi The journal of nursing.

[62]  Wilmar B. Schaufeli,et al.  The Burnout Companion to Study and Practice: A Critical Analysis , 1998 .

[63]  S. Ng,et al.  The role of holistic care culture in mitigating burnout and enhancing engagement: A study among elderly service workers in Hong Kong , 2011, Aging & mental health.

[64]  Eean R. Crawford,et al.  Linking job demands and resources to employee engagement and burnout: a theoretical extension and meta-analytic test. , 2010, The Journal of applied psychology.

[65]  A. Bakker,et al.  Stability and change in burnout: A 10‐year follow‐up study among primary care physicians , 2011 .

[66]  R. Harvey,et al.  The personality construct of hardiness, III: Relationships with repression, innovativeness, authoritarianism, and performance. , 2006, Journal of personality.

[67]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[68]  W. Schaufeli,et al.  Job burnout. , 2001, Annual review of psychology.

[69]  A. Shirom.  The Effects of Work Stress on Health , 2004 .

[70]  P. G. Monte,et al.  El síndrome de quemarse por el trabajo (burnout): grupos profesionales de riesgo , 2007 .

[71]  L. Pazvakawambwa,et al.  Forecasting methods and applications. , 2013 .

[72]  E. Lindberg.  Increased Job Satisfaction After Small Group Reflection on an Intensive Care Unit , 2007, Dimensions of critical care nursing : DCCN.

[73]  S. Kobasa,et al.  Stressful life events, personality, and health: an inquiry into hardiness. , 1979, Journal of personality and social psychology.

[74]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.