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Recovery home networks as social capital.

Leonard A JasonMayra GuerreroGabrielle LynchEd StevensMeghan Salomon-AmendJohn M Light
Published in: Journal of community psychology (2019)
Ensuring recovery home residents' social integration into a home environment is important for preventing early dropout and facilitating sustained recovery. Social capital theory may provide an explanation for how recovery homes may protect residents and improve recovery rates. However, little is known about how social capital in recovery home environments is structured and accessed. Recovery homes may increase social capital by sharing bonds through friendships, lending money, and advice-seeking. The current study describes social network cross sectional data obtained from a study of 42 Oxford House recovery homes, in three locations in the US (North Carolina, Texas, and Oregon). The residents rated each member of their house on the dimensions of friendship, money loaning, and advice seeking to assess how each resident views one another on these dimensions. The research used baseline data from a larger longitudinal study, and although some data were presented for the full sample (APL, isolates, mean reciprocity and density), the results primarily focused on case studies for three of the participating Oxford Houses-with examples of low, median, and high "connected" houses respectively. Standard measures of network structures were calculated for each home. Although all Oxford Houses follow the same house rules, they were found to vary in network structure. Findings indicated a considerable range of interconnectedness among residents in these houses, with friendship being the most common relationship, willingness to lend money less common, and advice-seeking the least common. The findings on friendship, willingness to lend, and advice-seeking provide promising leads about what occurs among the social networks within these complex eco-systems, and may provide ways to better understand and facilitate resident social integration into these settings.
Keyphrases
  • healthcare
  • mental health
  • cross sectional
  • mass spectrometry
  • machine learning