Intensive mothering in the time of coronavirus.
Published In: Journal of Social Issues, 2023, v. 79, n. 3. P. 997 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Lutz, Amy; Lee, Sujung; Bokayev, Baurzhan 3 of 3
Abstract
We investigated experiences of mothers of school‐age children in Central New York during a time of remote education due to COVID‐19. We extend the concept of intensive mothering, characterized by the expectation that mothers are constantly available to meet their children's needs, and examine mothers' intersectional identities related to their experience of remote education. Mothers working from home often went back and forth between work and school in what we refer to as a simultaneous shift. Essential workers were engaged in a sequential shift, engaging with children's schoolwork after work and trading off with partners. Mothers took on multiple roles during the pandemic which led to role strain. In extreme cases, multiple roles could be impossible to fill, leading to a situation of role conflict where the demands of one role made it impossible to meet the needs of another role. Mothers of children of color experienced more negative interactions with schools than White mothers. Mothers of children with disabilities spent extended time on remote schooling. A limitation of our study is that we only interviewed people in Central New York and cannot generalize the results of our research to a larger population. Another limitation to our approach was that we have little information on how fathers experienced work and overseeing children's schoolwork. Future research should examine how mothering may have changed after children returned to school. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Social Issues. 2023/09, Vol. 79, Issue 3, p997
- Document Type:Article
- Subject Area:Political Science
- Publication Date:2023
- ISSN:0022-4537
- DOI:10.1111/josi.12515
- Accession Number:172437323
- Copyright Statement:Copyright of Journal of Social Issues is the property of Wiley-Blackwell 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.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.