Transitions of Women's Substance Use Recovery Networks and 12-Month Sobriety Outcomes.
Soc Networks
; 63: 1-10, 2020 Oct.
Article
em En
| MEDLINE
| ID: mdl-32675917
ABSTRACT
Little is known about how the structure and composition of women's personal social networks (PSNs) combine to support recovery from substance use disorders, how PSNs change during early recovery, or how known covariates such as trauma, co-occurring mental health disorders, or treatment modality impact this relationship. This study used latent profile and transition analyses with 6 recovery-specific PSN indicators in a sample of women in early recovery (N=377) to identify three PSN typologies in relation to abstinence outcomes over 12 months, and track transitions between the typologies at 0-6 and 6-12 months. Women in the Highly Connected type (14.3%) had tightly-knit networks, more sober alters, and fewer treatment-related alters. Women in the Treatment-Related type (49.3%) had looser-knit networks with more sober and sobriety-supporting alters and alters they know from treatment. Women in the At-Risk type (36.3%) had more isolates, few sobriety-supporting alters, and more alters with whom they used. Women in the Treatment-Related Sobriety Support type were significantly more likely to maintain sobriety by 12 months (B=-0.81; OR=2.09, 95% CI [1.23-3.56]) than women in the At Risk type. Higher mean Trauma Symptom Checklist scores were positively related to membership in the At Risk type. The majority of women who transitioned did so by 6 months, with 41.6% transitioning then. Women in the At Risk group had the highest probability of transition (P=0.55). Being in residential treatment (versus outpatient) predicted lower odds of transitioning (B= -0.81, p=.06). This study provides a framework for conducting longitudinal latent variable analysis with social network data, and offers a clinically-useful starting point for research on individualized, targeted, and stage-based interventions for women in recovery.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Soc Networks
Ano de publicação:
2020
Tipo de documento:
Article