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Technology and Data Implications for the Public Health Workforce.
Martin, Laurie T; Chandra, Anita; Nelson, Christopher; Yeung, Douglas; Acosta, Joie D; Qureshi, Nabeel; Blagg, Tara.
  • Martin LT; RAND Corporation, Arlington, Virginia, USA.
  • Chandra A; RAND Corporation, Arlington, Virginia, USA.
  • Nelson C; RAND Corporation, Santa Monica, California, USA.
  • Yeung D; RAND Corporation, Santa Monica, California, USA.
  • Acosta JD; RAND Corporation, Arlington, Virginia, USA.
  • Qureshi N; Pardee RAND Graduate School, Santa Monica, California, USA.
  • Blagg T; Pardee RAND Graduate School, Santa Monica, California, USA.
Big Data ; 10(S1): S25-S29, 2022 09.
Article in English | MEDLINE | ID: covidwho-2151806
ABSTRACT
Achieving a modern equity-oriented public health system requires the development of a public health workforce with the skills and competencies needed to generate findings and integrate knowledge using diverse data. Yet current workforce capabilities and infrastructure are misaligned with what is needed to harness both new and older forms of data and to translate them into information that is equity contextualized. As with other articles in this supplement, this article builds from a literature review, environmental scan, and deliberations from the National Commission to Transform Public Health Data Systems. The article summarizes some of the challenges around current workforce capabilities and pipeline. The article identifies where the technology and data sectors can contribute skills, expertise, and assets in support of innovative workforce models and augment the development of public health workforce competencies.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Public Health / Health Workforce Type of study: Reviews Language: English Journal: Big Data Year: 2022 Document Type: Article Affiliation country: Big.2022.0208

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Public Health / Health Workforce Type of study: Reviews Language: English Journal: Big Data Year: 2022 Document Type: Article Affiliation country: Big.2022.0208