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TILES-2019: A longitudinal physiologic and behavioral data set of medical residents in an intensive care unit.
Yau, Joanna C; Girault, Benjamin; Feng, Tiantian; Mundnich, Karel; Nadarajan, Amrutha; Booth, Brandon M; Ferrara, Emilio; Lerman, Kristina; Hsieh, Eric; Narayanan, Shrikanth.
Afiliação
  • Yau JC; Signal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA. joannay@usc.edu.
  • Girault B; Signal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
  • Feng T; Signal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
  • Mundnich K; Signal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
  • Nadarajan A; Signal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
  • Booth BM; Signal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
  • Ferrara E; Information Sciences Institute (USC), Marina del Rey, CA, USA.
  • Lerman K; Information Sciences Institute (USC), Marina del Rey, CA, USA.
  • Hsieh E; Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Narayanan S; Signal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
Sci Data ; 9(1): 536, 2022 09 01.
Article em En | MEDLINE | ID: mdl-36050329
ABSTRACT
The TILES-2019 data set consists of behavioral and physiological data gathered from 57 medical residents (i.e., trainees) working in an intensive care unit (ICU) in the United States. The data set allows for the exploration of longitudinal changes in well-being, teamwork, and job performance in a demanding environment, as residents worked in the ICU for three weeks. Residents wore a Fitbit, a Bluetooth-based proximity sensor, and an audio-feature recorder. They completed daily surveys and interviews at the beginning and end of their rotation. In addition, we collected data from environmental sensors (i.e., Internet-of-Things Bluetooth data hubs) and obtained hospital records (e.g., patient census) and residents' job evaluations. This data set may be may be of interest to researchers interested in workplace stress, group dynamics, social support, the physical and psychological effects of witnessing patient deaths, predicting survey data from sensors, and privacy-aware and privacy-preserving machine learning. Notably, a small subset of the data was collected during the first wave of the COVID-19 pandemic.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Estresse Ocupacional / Internato e Residência Tipo de estudo: Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Estresse Ocupacional / Internato e Residência Tipo de estudo: Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article