Daria Panebianco1, Owen Gallupe2, Peter J Carrington3, Ivo Colozzi4. 1. National Addiction Centre, Addictions Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 4 Windsor Walk, Denmark Hill, London, England SE5 8BB, United Kingdom. Electronic address: daria.panebianco@kcl.ac.uk. 2. Department of Sociology and Legal Studies, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada N2L 3G1. Electronic address: ogallupe@uwaterloo.ca. 3. Department of Sociology and Legal Studies, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada N2L 3G1. Electronic address: pjc@uwaterloo.ca. 4. Department of Sociology and Business Law, Alma Mater Studiorum, University of Bologna, Strada Maggiore 45, 40125 Bologna, Italy. Electronic address: ivo.colozzi@unibo.it.
Abstract
BACKGROUND: The success of treatment for substance use issues varies with personal and social factors, including the composition and structure of the individual's personal support network. This paper describes the personal support networks and social capital of a sample of Italian adults after long-term residential therapeutic treatment for substance use issues, and analyses network correlates of post-treatment substance use (relapse). METHODS: Using a social network analysis approach, data were obtained from structured interviews (90-120 min long) with 80 former clients of a large non-governmental therapeutic treatment agency in Italy providing voluntary residential treatments and rehabilitation services for substance use issues. Participants had concluded the program at least six months prior. Data were collected on socio-demographic variables, addiction history, current drug use status (drug-free or relapsed), and the composition and structure of personal support networks. Factors related to risk of relapse were assessed using bivariate and multivariate logistic regression models. RESULTS: A main goal of this study was to identify differences between the support network profiles of drug free and relapsed participants. Drug free participants had larger, less dense, more heterogeneous and reciprocal support networks, and more brokerage social capital than relapsed participants. Additionally, a lower risk of relapse was associated with higher socio-economic status, being married/cohabiting, and having network members with higher socio-economic status, who have greater occupational heterogeneity, and reciprocate support. CONCLUSIONS: Post-treatment relapse was found to be negatively associated with the socioeconomic status and occupational heterogeneity of ego's support network, reciprocity in the ties between ego and network members, and a support network in which the members are relatively loosely connected with one another (i.e., ego possesses "brokerage social capital"). These findings suggest the incorporation into therapeutic programming of interventions that address those aspects of clients' personal support networks.
BACKGROUND: The success of treatment for substance use issues varies with personal and social factors, including the composition and structure of the individual's personal support network. This paper describes the personal support networks and social capital of a sample of Italian adults after long-term residential therapeutic treatment for substance use issues, and analyses network correlates of post-treatment substance use (relapse). METHODS: Using a social network analysis approach, data were obtained from structured interviews (90-120 min long) with 80 former clients of a large non-governmental therapeutic treatment agency in Italy providing voluntary residential treatments and rehabilitation services for substance use issues. Participants had concluded the program at least six months prior. Data were collected on socio-demographic variables, addiction history, current drug use status (drug-free or relapsed), and the composition and structure of personal support networks. Factors related to risk of relapse were assessed using bivariate and multivariate logistic regression models. RESULTS: A main goal of this study was to identify differences between the support network profiles of drug free and relapsed participants. Drug free participants had larger, less dense, more heterogeneous and reciprocal support networks, and more brokerage social capital than relapsed participants. Additionally, a lower risk of relapse was associated with higher socio-economic status, being married/cohabiting, and having network members with higher socio-economic status, who have greater occupational heterogeneity, and reciprocate support. CONCLUSIONS: Post-treatment relapse was found to be negatively associated with the socioeconomic status and occupational heterogeneity of ego's support network, reciprocity in the ties between ego and network members, and a support network in which the members are relatively loosely connected with one another (i.e., ego possesses "brokerage social capital"). These findings suggest the incorporation into therapeutic programming of interventions that address those aspects of clients' personal support networks.
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