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提升联邦公共卫生工作队伍的数据科学水平:疾病控制与预防中心的数据科学技能提升计划

Advancing Data Science Among the Federal Public Health Workforce: The Data Science Upskilling Program, Centers for Disease Control and Prevention.

作者信息

Bertulfo Mary Catherine P, Kirkcaldy Robert D, Franzke Laura H, Papagari Sangareddy Sridhar R, Reza Faisal

机构信息

Public Health Workforce Branch, Division of Workforce Development, National Center for State, Territorial, Local, and Tribal Public Health Infrastructure and Workforce (Ms Bertulfo and Drs Kirkcaldy and Franzke); Office of Science (Dr Papagari Sangareddy); and Informatics and Data Analytics Branch, Immunization Services Division, National Center for Immunization and Respiratory Diseases (Dr Reza), Centers for Disease Control and Prevention, Atlanta, Georgia.

出版信息

J Public Health Manag Pract. 2024;30(2):E41-E46. doi: 10.1097/PHH.0000000000001865.

Abstract

CONTEXT

Data can guide decision-making to improve the health of communities, but potential for use can only be realized if public health professionals have data science skills. However, not enough public health professionals possess the quantitative data skills to meet growing data science needs, including at the Centers for Disease Control and Prevention (CDC).

PROGRAM

The Data Science Upskilling (DSU) program increases data science literacy among staff and fellows working and training at CDC. The DSU program was established in 2019 as a team-based, project-driven, on-the-job applied upskilling program. Learners, within interdisciplinary teams, use curated learning resources to advance their CDC projects. The program has rapidly expanded from upskilling 13 teams of 31 learners during 2019-2020 to upskilling 36 teams of 143 learners during 2022-2023.

EVALUATION

All 2022-2023 cohort respondents to the end-of-project survey reported the program increased their data science knowledge. In addition, 90% agreed DSU improved their data science skills, 93% agreed it improved their confidence making data science decisions, and 96% agreed it improved their ability to perform data science work that benefits CDC.

DISCUSSION

DSU is an innovative, inclusive, and successful approach to improving data science literacy at CDC. DSU may serve as an upskilling model for other organizations.

摘要

背景

数据可以指导决策以改善社区健康状况,但只有当公共卫生专业人员具备数据科学技能时,数据的潜在用途才能得以实现。然而,没有足够的公共卫生专业人员具备定量数据技能来满足日益增长的数据科学需求,包括疾病控制与预防中心(CDC)的需求。

项目

数据科学技能提升(DSU)项目提高了在CDC工作和培训的工作人员及研究员的数据科学素养。DSU项目于2019年设立,是一个基于团队、以项目为驱动的在职应用技能提升项目。学习者在跨学科团队中使用精心策划的学习资源来推进他们在CDC的项目。该项目已迅速从2019 - 2020年提升31名学习者所在的13个团队的数据技能,扩展到2022 - 2023年提升143名学习者所在的36个团队的数据技能。

评估

所有参与2022 - 2023年项目的受访者在项目结束调查中表示,该项目增加了他们的数据科学知识。此外,90%的人同意DSU提升了他们的数据科学技能,93%的人同意它增强了他们做出数据科学决策的信心,96%的人同意它提高了他们开展对CDC有益的数据科学工作的能力。

讨论

DSU是一种创新、包容且成功的提高CDC数据科学素养的方法。DSU可以作为其他组织的技能提升模式。

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本文引用的文献

1
The Emergence and Future of Public Health Data Science.
Public Health Rev. 2021 Apr 26;42:1604023. doi: 10.3389/phrs.2021.1604023. eCollection 2021.
2
Addressing Challenges in Skills-based Education Through Innovation and Collaboration.
Am J Pharm Educ. 2021 Aug;85(7):8788. doi: 10.5688/ajpe8788.
3
Toward a New Strategic Public Health Science for Policy, Practice, Impact, and Health Equity.
Am J Public Health. 2021 Aug;111(8):1489-1496. doi: 10.2105/AJPH.2021.306355. Epub 2021 Jul 1.
4
Biomedical informatics meets data science: current state and future directions for interaction.
JAMIA Open. 2018 Aug 9;1(2):136-141. doi: 10.1093/jamiaopen/ooy032. eCollection 2018 Oct.
6
Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century.
Ethn Dis. 2017 Apr 20;27(2):95-106. doi: 10.18865/ed.27.2.95. eCollection 2017 Spring.
7
Interprofessional education: a concept analysis.
Adv Med Educ Pract. 2010 Nov 25;1:75-84. doi: 10.2147/AMEP.S13207. Print 2010.

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