Application of artificial neural networks to a study of nursing burnout

Nursing is generally considered to be a profession with high levels of emotional and physical stress that tend to increase. These high stress levels lead to a high risk of burnout. The objective was to assess whether artificial neural network (ANN) paradigms offer greater predictive accuracy than statistical methodologies, which are commonly used in the field of burnout. A radial basis function (RBF) network and hierarchical stepwise regression was used to assess burnout. The comparison of the two methodologies was carried out by analysing a sample of 462 nurses and student nurses. The subjects were from three hospitals in Madrid (Spain), who completed the 'Nursing Burnout Scale' survey. A RBF network was better suited for the analysis of burnout than hierarchical stepwise regression. The outcomes indicate furthermore that the relationship with the burnout process of the predictive variables age, job status, workload, experience with pain and death, conflictive interaction, role ambiguity and hardy personality is not entirely linear. The usage of ANNs in the field of burnout has been justified due to their superior ability to capture non-linear relationships, which is relevant for theory development. STATEMENT OF RELEVANCE: Due to the superior ability to capture non-linear relationships, ANNs are better suited to explain and predict burnout and its subdimensions than common statistical methods. From this perspective, more specific programmes to prevent burnout and its consequences in the workplace can be designed.