2023
DOI: 10.1080/10530789.2023.2174565
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Predictors of housing instability and stability among Housing First participants: A 24-month study

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Cited by 6 publications
(5 citation statements)
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“…Our findings highlight limitations in the current design of homelessness algorithms. Historically, computational homelessness research often used GLMs to identify common characteristics (or types) of unhoused individuals [27,47,50,53,97,107,141]. Our findings showed that this popular methodology has been extended to designing street-level homelessness algorithms due to their explainability.…”
Section: Inferential Statisticsmentioning
confidence: 84%
See 1 more Smart Citation
“…Our findings highlight limitations in the current design of homelessness algorithms. Historically, computational homelessness research often used GLMs to identify common characteristics (or types) of unhoused individuals [27,47,50,53,97,107,141]. Our findings showed that this popular methodology has been extended to designing street-level homelessness algorithms due to their explainability.…”
Section: Inferential Statisticsmentioning
confidence: 84%
“…For example, Roebuck et al [107], Shelton et al [117], and Wong et al [141] used regression models to determine risk factors associated with homelessness. Culhane and Kuhn [68] used cluster analysis to identify three types of shelter users (transitional, episodic, and chronic).…”
Section: Related Workmentioning
confidence: 99%
“…These findings align with recent research that has identified overdose as a serious concern in supportive housing programs [ 23 , 41 ]. More broadly, substance use problem severity has been identified as a risk factor associated with housing instability in PSH and continued connections to people who use substances may present eviction and apartment takeover risks [ 15 , 75 ]. The latter findings highlight the complexity of social networks among people who use substances in PSH, as both potential sources of important support and risk [ 76 , 77 ].…”
Section: Discussionmentioning
confidence: 99%
“…In an early examination of data from a multisite randomized controlled trial of Housing First, an intervention often delivered as PSH, findings were only able to predict 3.8% of the variance in housing instability outcomes after 12 months using sociodemographic, clinical, and housing history variables [ 14 ]. Subsequent analyses from the same trial with a more stringent definition of housing stability and set of predictors produced an improved model, but ultimately yielded the same conclusion: Although certain individual characteristics are risks factors associated with difficulties establishing housing stability, the researchers concluded that it was not possible to accurately predict who would be unsuccessful in Housing First after 24 months [ 15 ]. Studies examining associations between service use and housing stability have also produced relatively small effect sizes [ 16 ].…”
Section: Introductionmentioning
confidence: 99%
“…The model assesses predisposing factors (e.g., personal characteristics such as age and gender), need factors (e.g., mental and physical health conditions), enabling factors (e.g., employment, housing), and health behaviors (e.g., use of services). Based on Gabrielian et al’s (2016) and Roebuck et al’s (2023) use of the Gelberg–Andersen Model, we defined the health behavior domain as access to health services, specifically, access to services in the study agency (e.g., program dose, contacts, program completion, and the use of other agency programs). All the factors in Gelberg–Andersen’s model are posited to influence health and health service outcomes (Gabrielian et al, 2016; Gelberg et al, 2000).…”
mentioning
confidence: 99%