The Need for Historical Fluency in Pandemic Law and Policy.
Published In: Journal of the History of Medicine & Allied Sciences, 2024, v. 79, n. 4. P. 407 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Goldberg, Daniel S 3 of 3
Abstract
The primary claim of this essay is that historical fluency is required for effective work in crafting legal and policy interventions as a part of public health emergency preparedness and response (PHEPR). At a broad level, public health law is explicitly recognized as a key systems-level component of PHEPR practice. 1 This essay therefore focuses on the extent to which historical fluency is necessary or at least useful to all aspects of PHEPR that draw on or deploy legal and policy mechanisms (e.g. design, planning, implementation, dissemination, monitoring and evaluation, etc.). The essay collectively refers to these legal and policy mechanisms as epidemic law and policy response (ELAPR). Part I explains the concept of historical fluency. Part II explores the foundations of public health law both as a way of highlighting key structural features of ELAPR and in supporting the claim that historical fluency is critical for ELAPR. Part III applies the previous arguments to a specific case study to highlight the promise and power of historical fluency – the outbreak of bubonic plague in San Francisco in 1900. Tracking this essay's pragmatic focus, part IV offers several recommendations for how specifically historical fluency in public health law and ethics can be operationalized in PHEPR practice and policy. Part V summarizes and concludes. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of the History of Medicine & Allied Sciences. 2024/10, Vol. 79, Issue 4, p407
- Document Type:Article
- Subject Area:History
- Publication Date:2024
- ISSN:0022-5045
- DOI:10.1093/jhmas/jrae009
- Accession Number:180268040
- Copyright Statement:Copyright of Journal of the History of Medicine & Allied Sciences is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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