Objective: A public health workforce taxonomy was published in 2014 to provide a standardized mechanism for describing public health worker characteristics. The Public Health Workforce Interests and Needs Survey (PH WINS) used 7 of the taxonomy's 12 axes as a basis for its survey response choices, 3 of which are the focus of this analysis. The purpose of this study was to determine the relative utility, reliability, and accuracy of the public health workforce taxonomy in categorizing local and state public health workers using a survey tool. This specifically included the goal of reducing the number of responses classified as "other" occupation, certification, or program area by recoding responses into taxonomy categories and determining potential missing categories for recommendation to the advisory committee that developed the taxonomy.
Design: Survey questions associated with the occupation, certification, and program area taxonomy axes yielded qualitative data from respondents who selected "other." The "other" responses were coded by 2 separate research teams at the University of Michigan Center of Excellence in Public Health Workforce Studies and NORC at the University of Chicago.
Main outcome measures: Researchers assigned taxonomy categories to all analyzable qualitative responses and assessed the percentage of PH WINS responses that could be successfully mapped to taxonomy categories.
Results: Between respondent self-selection and research team recoding, the public health workforce taxonomy successfully categorized 95% of occupation responses, 75% of credential responses, and 83% of program area responses. Occupational categories that may be considered for inclusion in the taxonomy in the future include disease intervention specialists and occupations associated with regulation, certification, and licensing.
Conclusions: The public health workforce taxonomy performed remarkably well in categorizing worker characteristics in its first use in a national survey. The analysis provides some recommendations for future taxonomy refinement.