School of Public Health, George Washington University, Washington, DC, USA.
School of Social Work, University of Maryland Baltimore, Baltimore, MD, USA.
New Solut. 2024 Nov;34(3):172-181. doi: 10.1177/10482911241269313. Epub 2024 Aug 9.
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 The national information systems used to understand health status and health behaviors, including drug use, rely on outdated census categories for self-employed workers.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.
本文描述了从几项美国重要的国家健康和行为调查中收集到的与工作相关的信息，重点介绍了阻碍识别零工经济中弱势工人反应的数据空白，特别强调了劳动力中不断增长的数字平台部门。用于了解健康状况和健康行为（包括吸毒）的国家信息系统依赖于过时的个体经营者人口普查类别。本文描述了了解这一不断增长的劳动力部门需求的重要性，并说明了一些最知名和常用的国家调查如何未能满足这一需求。对于开展国家健康和行为调查的机构，我们建议修改用于分类工人类型的类别，并建议采用新的工人-雇主关系分类模型。
