JOURNAL ARTICLE
Getting Honest about Abortion and Disability in Bioethics—Narratives of Identity-Based Retributory Suffering and Saying the Quiet Part Out Loud.
Published In: IJFAB: International Journal of Feminist Approaches to Bioethics, 2026, v. 19, n. 1. P. 122 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Schmid, Spencer 3 of 3
Abstract
This article critically examines traditional bioethical approaches to abortion and disability, arguing that these classical frameworks—centered on personhood, rights, and obligations—are inadequate in the wake of the 2022 Dobbs v. Jackson Women's Health Organization ruling and the COVID-19 pandemic. It highlights how nonideal bioethics, which attends to real-world sociopolitical complexities, reveals a shared underlying narrative of "identity-based retributory suffering," wherein individuals are seen as deserving harm because of their identities as people who seek abortions or as disabled persons. The article documents how abortion restrictions post-Dobbs have led to increased unsafe abortions and systemic harms, while pandemic responses have exacerbated ableist and eugenic attitudes that devalue disabled lives. It concludes by advocating for bioethics to embrace narrative repair—centering counternarratives from affected communities—to more honestly address and ameliorate these harms rather than perpetuate outdated, harmful discourses.
Additional Information
- Source:IJFAB: International Journal of Feminist Approaches to Bioethics. 2026/04, Vol. 19, Issue 1, p122
- Document Type:Article
- Subject Area:Religion and Philosophy
- Publication Date:2026
- ISSN:1937-4585
- DOI:10.3138/ijfab-2025-0020
- Accession Number:193401757
- Copyright Statement:Copyright of IJFAB: International Journal of Feminist Approaches to Bioethics is the property of University of Toronto Press 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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