title: Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.
abstract: 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.
  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.
authors.affiliation: a Department of Psychology , IE University, Cardenal Zúñiga , Segovia , Spain.
authors.forename: Felix
authors.initials: F
authors.lastname: Ladstätter
authors.affiliation: b Department of Psychology, Universidad Autónoma de Madrid , Madrid , Spain.
authors.forename: Eva
authors.initials: E
authors.lastname: Garrosa
authors.affiliation: b Department of Psychology, Universidad Autónoma de Madrid , Madrid , Spain.
authors.forename: Bernardo
authors.initials: B
authors.lastname: Moreno-Jiménez
authors.affiliation: b Department of Psychology, Universidad Autónoma de Madrid , Madrid , Spain.
authors.forename: Vicente
authors.initials: V
authors.lastname: Ponsoda
authors.affiliation: c Department of Psychology, Universidad Nacional de Educación a Distancia , Madrid , Spain.
authors.forename: José Manuel
authors.initials: JM
authors.lastname: Reales Aviles
authors.affiliation: d School of Public Health, Fudan University , Shanghai , China.
authors.forename: Junming
authors.initials: J
authors.lastname: Dai
mesh_terms: D000328:Adult; D002055:Burnout, Professional / Q000523:psychology*; D002681:China; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D009726:Nurses / Q000523:psychology*; D009787:Occupational Medicine / Q000379:methods*
keywords: artificial neural network; burnout; hardiness; health services; sensitivity analysis