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Record W3033970593 · doi:10.1016/s2468-2667(20)30055-4

Effectiveness of permanent supportive housing and income assistance interventions for homeless individuals in high-income countries: a systematic review

2020· review· en· W3033970593 on OpenAlexaff
Tim Aubry, Gary Bloch, Vanessa Brcic, Ammar Saad, Olivia Magwood, Tasnim Abdalla, Qasem Alkhateeb, Edward Xie, Christine Mathew, Terry Hannigan, Chris Costello, Kednapa Thavorn, Vicky Stergiopoulos, Peter Tugwell, Kevin Pottie

Bibliographic record

VenueThe Lancet Public Health · 2020
Typereview
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsOttawa HospitalMcGill UniversityUniversity of British ColumbiaOttawa Public HealthUniversity Health NetworkUniversity of TorontoSt. Michael's HospitalCentre for Addiction and Mental HealthBruyèreUniversity of Ottawa
Fundersnot available
KeywordsPsychological interventionHigh income countriesMedicineGerontologyDeveloping countryEconomic growthPsychiatryEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Permanent supportive housing and income assistance are valuable interventions for homeless individuals. Homelessness can reduce physical and social wellbeing, presenting public health risks for infectious diseases, disability, and death. We did a systematic review, meta-analysis, and narrative synthesis to investigate the effectiveness and cost-effectiveness of permanent supportive housing and income interventions on the health and social wellbeing of individuals who are homeless in high-income countries. METHODS: We searched MEDLINE, Embase, CINAHL, PsycINFO, Epistemonikos, NIHR-HTA, NHS EED, DARE, and the Cochrane Central Register of Controlled Trials from database inception to Feb 10, 2020, for studies on permanent supportive housing and income interventions for homeless populations. We included only randomised controlled trials, quasi-experimental studies, and cost-effectiveness studies from high-income countries that reported at least one outcome of interest (housing stability, mental health, quality of life, substance use, hospital admission, earned income, or employment). We screened studies using a standardised data collection form and pooled data from published studies. We synthesised results using random effects meta-analysis and narrative synthesis. We assessed certainty of the evidence using the Grading of Recommendations Assessment, Development, and Evaluation approach. FINDINGS: Our search identified 15 908 citations, of which 72 articles were included for analysis (15 studies on permanent supportive housing across 41 publications, ten studies on income interventions across 15 publications, and 21 publications on cost or cost-effectiveness). Permanent supportive housing interventions increased long-term (6 year) housing stability for participants with moderate support needs (one study; rate ratio [RR] 1·13 [95% CI 1·01-1·26]) and high support needs (RR 1·42 [1·19-1·69]) when compared with usual care. Permanent supportive housing had no measurable effect on the severity of psychiatric symptoms (ten studies), substance use (nine studies), income (two studies), or employment outcomes (one study) when compared with usual social services. Income interventions, particularly housing subsidies with case management, showed long-term improvements in the number of days stably housed (one study; mean difference at 3 years between intervention and usual services 8·58 days; p<0·004), whereas the effects on mental health and employment outcomes were unclear. INTERPRETATION: Permanent supportive housing and income assistance interventions were effective in reducing homelessness and achieving housing stability. Future research should focus on the long-term effects of housing and income interventions on physical and mental health, substance use, and quality-of-life outcomes. FUNDING: Inner City Health Associates.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.043
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.013
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.157
GPT teacher head0.490
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations214
Published2020
Admission routes1
Has abstractyes

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