Abstract

Hospitals are high-stress environments where workers face a high risk of occupational burnout due to a mix of imbalanced schedules, understaffing, and emotional stress. In this paper, we propose a computational framework to infer the latent psychological makeup and traits of hospital workers. We apply machine learning models to psychometric data obtained from a suite of psychological survey instruments, collected as a part of TILES, a ten-week research study carried out in a large Los Angeles hospital. The study population represents over 200 hospital employees, including nurses and those in administrative positions. A computational framework that combines clustering and non-negative matrix factorization was used to extract the latent interplay between psychological constructs along dimensions of health, affect, personality, cognitive ability, and job performance. We illustrate how the proposed framework …

Date

November 12, 2018

Authors

Hsien-Te Kao, Homa Hosseinmardi, Shen Yan, Michelle Hasan, Shrikanth Narayanan, Kristina Lerman, Emilio Ferrara

Conference

2018 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC)

Pages

156-161

Publisher

IEEE