ReviewFrontiers in public health2026
Building data science capacity in the public health workforce: a Public Health Data Science Life Cycle framework.
Review in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The digital transformation of public health systems has increased the need for a workforce capable of using data science to inform population health decision-making. However, public health workforce development efforts have not fully integrated data science competencies into training and professional development pathways. This paper presents a competency mapping framework, and a proposed national training agenda designed to operationalize data science skills for the public health workforce. Drawing on and synthesizing existing public health competency frameworks, accreditation standards, and governmental public health workforce task analyses, competencies were mapped to an eight-stage Public Health Data Science Life Cycle. Competency gaps were then identified, and additional competencies were developed to address emerging needs in public health data science practice. The analysis found that existing competencies are concentrated in the middle stages of the Public Health Data Science Life Cycle - particularly data collection and management, data analysis and modeling, and data interpretation and implications - with fewer competencies addressing earlier stages, such as problem framing, and later stages, including communication and life cycle preservation. Key gaps were identified in areas including project feasibility and problem definition, data governance and interoperability, bias and ethical data use, data interpretation and visualization, and the communication and translation of findings to inform policy and community action. Building on these findings, this paper proposes a practical framework to guide the integration of public health data science competencies into workforce training and continuing professional learning. By aligning competencies with the full life cycle of public health data science practice, this approach supports a more comprehensive and applied model for workforce development, with the goal of strengthening data-informed decision-making and improving population health outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.