Willingness to lend resources is associated with increases in recovery and participation in community activities

ABSTRACT

Recovery homes may facilitate individuals with substance use disorders re-integration back into community settings by providing friendship, resources, and advice. Participants of the current study were over 600 residents of 42 Oxford House recovery homes. Findings indicated that willingness to share resources in the form of loans was associated with higher levels of house involvement in recovery home chapters. Active involvement in house and community affairs may influence more recovery within homes or may be an indicator of houses with residents with more capacities and skills for positive long-term health outcomes. Such findings suggest that recovery is a dynamic process with multiple ecological layers embedding individuals, their immediate social networks, and the wider community.

KEYWORDS:

• Community psychology
• ecological
• Oxford houses
• recovery homes
• substance use disorders

Acknowledgments

This work was supported in part by the National Institute on Alcohol Abuse and Alcoholism grant AA022763. The authors appreciate the social network help of Ed Stevens. We also acknowledge the help of several members of the Oxford House organization, and in particular Paul Molloy, Alex Snowden, Casey Longan, and Howard Wilkins.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ A SAOM consists of a set of equations, one for each endogenous variable, expressed as a function of a set of predictors – referred to by the software, and in later parts of this article as ‘effects.’ The equations can share common endogenous or exogenous variables and can thus be interlinked in useful ways to model social evolution.

² The equations in SAOMs can be seen as functions describing the utility the actors place on being associated with particular configurations of their local network and their behaviors. Predictors in SAOMs are the dimensions on which ‘preference’ can vary. For example, if the researcher believes actors have a preference related to being engaged in reciprocal relationships, a relevant predictor should be included in the model. The term ‘preference’ maybe (though in general need not be) taken literally if doing so is theoretically appropriate; in the present study, we adopt such an interpretation of effects, as it appears to dovetail well with the types of choices available to recovering individuals in sober-living homes.

³ Fit is assessed by testing how well the model reproduces large-scale structural or behavioral features of the data which are not explicitly modeled. For instance, a well-fitting model should reproduce network statistics such as the proportion of reciprocated relationships, triadic structures, and so on (Lospinoso & Snijders, 2019). The approach measures statistical agreement defined as Mahalanobis Distance. The standard suite of structural features used in goodness-of-fit checking includes outdegree distribution (total number of others chosen by ego), indegree distribution (total number of alters who choose ego), and the triad census (all possible triadic structures). More details on the description of the data are included in Doogan et al. (2019).

⁴ The model examined predictors of changes in the recovery factor, and how these variables are inter-related to each other. For RF, the linear and quadratic shape effects represent the location and shape of the recovery factor distribution for the referent group when all other recovery factor model terms are set to zero. For negative exit, we model the rate of change, so these effects are not relevant.

⁵ This outcome is modeled as a generalized Cox Regression proportional hazard formulation (Cox, 1972; Greenan, 2015) with a predictor-based time-to-departure rate as the (proportional) hazard rate parameterization. That is, overall between-wave rates of change can be interpreted as non-contingent rates of house departure, which may be modified by some aspects of ego’s network embedding or behavioral/demographic characteristics.