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Improving Identification of Gig Workers in National Health and Behavior Surveys.

Michael Chidera OfoneduJodi J FreyOrrin D WareKathleen HokeClifford S MitchellMarianne Cloeren
Published in: New solutions : a journal of environmental and occupational health policy : NS (2024)
This paper describes the work-related information collected in several important U.S. national health and behavior surveys, to highlight data gaps that prevent identifying responses by vulnerable workers in the gig economy, with emphasis on the growing digital platform sector of the work force. The national information systems used to understand health status and health behaviors, including drug use, rely on outdated census categories for self-employed workers. This paper describes the importance of understanding the needs of this growing part of the labor sector and describes how some of the most well-known and utilized national surveys fail to meet this need. For the agencies conducting national health and behavior surveys, we propose revisions to the categories used to classify type of worker and recommend adoption of a new Worker-Employer Relationship Classification model.
Keyphrases
  • cross sectional
  • health information
  • quality improvement
  • electronic health record
  • healthcare
  • public health
  • mental health
  • deep learning
  • high throughput
  • single cell