JOURNAL ARTICLE

Belief-based denial of contraception and abortion care in Canada: A scoping review.

  • Published In: Canadian Journal of Human Sexuality, 2024, v. 33, n. 2. P. 211 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Dixit, Anvita; Suvarna, Dipesh; Arthur, Joyce; Foster, Angel M. 3 of 3

Abstract

This article focuses on the belief-based denial of contraception and abortion care in Canada, examining existing knowledge through a scoping review of 97 sources including peer-reviewed articles, media reports, and reproductive health organization materials. Although Canadian federal policies classify contraception and abortion as medically necessary services, healthcare providers are permitted to refuse these services or referrals based on personal beliefs, a practice often termed "conscientious objection," though the article prefers "belief-based denial" to better reflect the medical context. The literature primarily addresses conceptual definitions, legal and ethical frameworks, and policy variability across provinces, revealing unclear and inconsistent guidelines regarding providers' duty to refer patients when denying care. Notably, there is a significant gap in research documenting the experiences and impacts of belief-based denial on patients seeking contraception and abortion services in Canada. The article highlights ongoing debates about terminology and calls for further research centered on the perspectives of those affected to inform equitable policy development.

Additional Information

  • Source:Canadian Journal of Human Sexuality. 2024/09, Vol. 33, Issue 2, p211
  • Document Type:Article
  • Subject Area:Consumer Health
  • Publication Date:2024
  • ISSN:1188-4517
  • DOI:10.3138/cjhs-2023-0055
  • Accession Number:180087215
  • Copyright Statement:Copyright of Canadian Journal of Human Sexuality 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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