Dynamic interdependence of advice seeking, loaning, and recovery characteristics in recovery homes

ABSTRACT

Recovery homes in the US provide stable housing for over 250,000 individuals with past histories of homelessness, psychiatric comorbidity and criminal justice involvement. We need to know more about how these settings help those who remain in recovery. Our study measured advice seeking and willingness-to-loan relationships, and operationalized them as a dynamic multiplex social network—multiple, simultaneous interdependent relationships—that exist within 42 Oxford House recovery homes over time. By pooling relationship dynamics across recovery houses, a Stochastic Actor-Oriented Modeling (SAOM) framework was used to estimate a set of parameters governing the evolution of the network and the recovery attributes of the nodes simultaneously. Findings indicated that advice and loan relationships and recovery-related attitudes were endogenously interdependent, and these results were affected exogenously by gender, ethnicity, and reason for leaving the recovery houses. Prior findings had indicated that higher advice seeking in recovery houses was related to higher levels of stress with more negative outcomes. However, the current study found that recovery is enhanced over time if advice was sought from residents with higher recovery scores. Our study shows that social embedding, i.e., one’s position in relationship networks affects recovery prospects. More specifically, the formation of ties with relatively more recovered residents is an important predictor of better outcomes.

KEYWORDS:

• Advice seeking
• loaning
• recovery homes
• oxford houses
• recovery latent factor
• social networks
• stochastic actor oriented modeling

Acknowledgments

The authors appreciate the social network help of Ed Stevens, Mayra Guerrero, Meghan Salomon-Amend, Mike Stoolmiller, Joseph Cotler, and Mohammed Islam. 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.

Notes

1. 34 residents had more than one OH exit, either from two different OH’s or from the same OH. To avoid greatly complicating our model for a very small number of individuals, we only included their first exit in all analyses.

2. Each social network relationship type was measured with a 5-point scale. Participant’s ratings were represented by an adjacency matrix with each row representing the ratings provided by an individual and each column representing the ratings received by an individual. The SAOM analytic strategy required all rating values to be dichotomized (0 = no relationship present; 1 = relationship present) and entered as a corresponding element of the matrix.

3. Mahalanobis distance is a generalization of Euclidean distance that additionally accounts for correlation among the dimensions of a multivariate statistic. This is useful here because when characterizing the network with a multidimensional statistic, a single tie change to the network could affect multiple dimensions of the statistic in different—but correlated—ways. Failing to account for correlations could bias the calculated distance in either direction depending on the nature of the correlation structure.

4. Total alter is a measure of how ego’s score is affected by whether alters’ scores are on average greater or lesser than the overall sample mean. A positive estimate indicates that egos tend to have RF scores in the same direction away from the mean as their alters, and does not differentiate whether that is an increase or decrease, or whether ego becomes more similar or more dissimilar from their alters (e.g., the latter would occur if ego moves further from the mean than alters).

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.

6. Note that it would be possible for all of ego’s peers to have lower recovery factor scores than ego. But because the sum of their recovery factors is higher than ego’s recovery score, ego’s RF should improve. This could mean that, for example, ego gets different value from each individual alter, and it may not matter whether alter has a higher recovery factor at all. In other words, the effects depend on one’s alters’ recovery factor scores relative to one’s own. Even if not a single alter has a higher recovery factor than ego, the model indicates that ego may still increase the recovery factor as a result of the distribution of alters’ recovery factors. This is intriguing especially since the average-alter influence effect was not significant, as noted earlier. Nevertheless, it is fair to say that if one affiliates with alters whose recovery factor is better than their own, theirs will improve, all else being equal.

Additional information

Funding

The authors appreciate the financial support from the National Institute on Alcohol Abuse and Alcoholism [grant number AA022763].