Peer-Reviewed Publication
Am J Public Health2026;116(S4):S266-S271.September 1, 2026Journal Article

"Invest in Us!" Exploring the Well-Being and Sustainability of the Community Health Worker Workforce.

Ada M Wilkinson-Lee1, Katie C Stalker1, Susan Mayfield-Johnson1, Toya Graham1, Nury Stemple1, Zoe Somerville1, Dana R Hunter1, Shanondora Billiot1, Mary-Ellen Brown1
1Ada M. Wilkinson-Lee is with the Department of Mexican American Studies, The University of Arizona, Tucson. Katie C. Stalker is with the University of Buffalo, School of Social Work, Buffalo, NY. Susan Mayfield-Johnson is with the Dr Lynn Cook Hartwig Public Health Program, The University of Southern Mississippi, Hattiesburg. Toya Graham is with the Carolyn W. and Charles T. Beaird Family Foundation, Shreveport, LA. Nury Stemple was with Arizona State University, Tucson. Zoe Somerville is with the Global Center for Applied Health Research, Arizona State University, Phoenix. Dana R. Hunter and Mary-Ellen Brown are with the Steve Hicks School of Social Work at The University of Texas at Austin. Shanondora Billiot is with the School of Social Work, Watts College of Public Service & Community Solutions, Arizona State University, Phoenix.

Abstract

This article presents findings from a qualitative study of the Community Health Workers for COVID Response and Resilient Communities project in the United States. We conducted 27 interviews with community health workers (CHWs), recipient program staff, and program partners. Thematic analysis revealed two main themes-organizational capacity and political support-and four subthemes. We illustrate pa…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.