CONDITIONS FOR ACCESS TO PEOPLE WITH DISABILITIES IN NURSING INSTITUTIONS: USE OF ARTIFICIAL NEURAL NETWORKS AS DECISION SUPPORT

Abstract

Objective: To investigate the conditions of accessibility in three undergraduate nursing institutions in the city of Joao Pessoa. Material and Methods: This was an exploratory, descriptive and inferential study using artificial neural network systems such as Multilayer Perceptron (MLP). The sample consisted of students with disabilities and other academics from three undergraduate nursing courses in the city of Joao Pessoa, in the period between August 2008 and June 2009. For empirical data collection, it was used a structured questionnaire comprising socioeconomic data, accessibility and inclusion policies. Results: Access conditions for people with disabilities was considered “weak” (69.96%), presenting percentage of accuracy satisfactory (86.0987%) and significant statistic Kappa (approximately 0.7), showed by the artificial neural network type Multilayer Perceptron. Conclusion: The accessibility conditions of nursing students in the scenarios investigated were considered "weak" with significant predictors when artificial neural network systems were used as support for decision making. DESCRIPTORS: Education, Nursing. Higher Education Institutions. Decision Support Techniques.