Literature DB >> 33689101

Negative Recovery Capital in Gambling Disorder: A Conceptual Model of Barriers to Recovery.

Belle Gavriel-Fried1, Niva Lev-El2.   

Abstract

Recovery from any addiction, including gambling, involves continuous efforts to improve wellness despite the obstacles and challenges. The recovery capital (RC) model is a holistic conceptual framework operationalized along a continuum ranging from positive internal and external RC resources that enhance recovery to negative recovery capital (NRC) which are defined as obstacles that impede individuals from coping with their addiction. Although previous studies have conceptualized the positive side of the RC model, no study has systematically explored NRC. This study develops a model that maps and conceptualizes the elements that impede recovery from gambling disorder (GD). In the current study, 133 individuals with a lifetime GD were interviewed about the factors that have hindered their recovery. A content analysis identified 14 NRC categories that were classified into four domains: Human NRC (urges and uncontrolled urges, cognitive distortions, inaction, sensation seeking, stressful life events, negative emotions, ability to conceal, a lack of motivation to recover), Social NRC (lack of social or familial networks, conflictual or dangerous social networks), Community NRC (an environment that encourages gambling, money lenders), and Financial NRC (financial distress and debt, money as a risk factor). The discussion centers on a holistic perspective of the elements that hinder recovery from GD, and calls for intervention methods that aim to minimize NRC to provide more holistic solutions to GD and possibly other addictions.
© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.

Entities:  

Keywords:  Addiction; Gambling disorder; Negative recovery capital; Recovery

Mesh:

Year:  2021        PMID: 33689101     DOI: 10.1007/s10899-021-10016-3

Source DB:  PubMed          Journal:  J Gambl Stud        ISSN: 1050-5350


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