Struggling to manage: A constructivist grounded theory of hoarding behaviours.
Published In: Journal of Community & Applied Social Psychology, 2023, v. 33, n. 5. P. 1280 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Ruby‐Granger, Victoria; Wilde, David; Seymour‐Smith, Sarah; Zysk, Eva 3 of 3
Abstract
Hoarding behaviours can cause numerous problems including health risks, family conflict, and removal of children and pets from the home. Hoarding research typically adopts a cognitive‐behavioural framework and uses quantitative methods; we aimed to further understand the development of hoarding behaviour from a qualitative perspective. Constructivist grounded theory methods were employed across two phases of data collection via semi‐structured interviews with participants identifying as exhibiting hoarding behaviours. Provisional categories were developed in phase one; further data analysis in phase two helped to establish our grounded theory. The theoretical core is a struggle to manage possessions and life, including life transitions such as moving to a new home and starting or finishing university. 'Struggling to manage' incorporates emotional struggles with possessions and the impacts of personal trauma and overwhelming life events. A further category, 'Trying to overcome hoarding' incorporates participants' efforts to manage and overcome their hoarding. Findings highlight the importance of viewing hoarding in a holistic context. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Community & Applied Social Psychology. 2023/09, Vol. 33, Issue 5, p1280
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2023
- ISSN:1052-9284
- DOI:10.1002/casp.2721
- Accession Number:171960889
- Copyright Statement:Copyright of Journal of Community & Applied Social Psychology 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.)
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