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Record W3045243042 · doi:10.1097/phh.0000000000001219

Permanent Supportive Housing With Housing First to Reduce Homelessness and Promote Health Among Homeless Populations With Disability: A Community Guide Systematic Review

2020· review· en· W3045243042 on OpenAlexaboutno aff
Yinan Peng, Robert A. Hahn, Ramona Finnie, Jamaicia Cobb, Samantha P. Williams, Jonathan E. Fielding, Robert L. Johnson, Ann Elizabeth Montgomery, Alex Schwartz, Carles Muntaner, Veronica Helms Garrison, Beda Jean‐Francois, Benedict I. Truman, Mindy T. Fullilove

Bibliographic record

VenueJournal of Public Health Management and Practice · 2020
Typereview
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
FundersNational Institutes of Health
KeywordsSupportive housingHousing FirstMental healthGovernment (linguistics)Inclusion (mineral)GerontologyPsychiatryMedicineSubstance abuseAffordable housingHealth carePsychologyMental illnessPolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

CONTEXT: Poor physical and mental health and substance use disorder can be causes and consequences of homelessness. Approximately 2.1 million persons per year in the United States experience homelessness. People experiencing homelessness have high rates of emergency department use, hospitalization, substance use treatment, social services use, arrest, and incarceration. OBJECTIVES: A standard approach to treating homeless persons with a disability is called Treatment First, requiring clients be "housing ready"-that is, in psychiatric treatment and substance-free-before and while receiving permanent housing. A more recent approach, Housing First, provides permanent housing and health, mental health, and other supportive services without requiring clients to be housing ready. To determine the relative effectiveness of these approaches, this systematic review compared the effects of both approaches on housing stability, health outcomes, and health care utilization among persons with disabilities experiencing homelessness. DESIGN: A systematic search (database inception to February 2018) was conducted using 8 databases with terms such as "housing first," "treatment first," and "supportive housing." Reference lists of included studies were also searched. Study design and threats to validity were assessed using Community Guide methods. Medians were calculated when appropriate. ELIGIBILITY CRITERIA: Studies were included if they assessed Housing First programs in high-income nations, had concurrent comparison populations, assessed outcomes of interest, and were written in English and published in peer-reviewed journals or government reports. MAIN OUTCOME MEASURES: Housing stability, physical and mental health outcomes, and health care utilization. RESULTS: Twenty-six studies in the United States and Canada met inclusion criteria. Compared with Treatment First, Housing First programs decreased homelessness by 88% and improved housing stability by 41%. For clients living with HIV infection, Housing First programs reduced homelessness by 37%, viral load by 22%, depression by 13%, emergency departments use by 41%, hospitalization by 36%, and mortality by 37%. CONCLUSIONS: Housing First programs improved housing stability and reduced homelessness more effectively than Treatment First programs. In addition, Housing First programs showed health benefits and reduced health services use. Health care systems that serve homeless patients may promote their health and well-being by linking them with effective housing services.

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)0.009
metaresearch head score (Gemma)0.038
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 score0.022
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0150.016
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.231
GPT teacher head0.495
Teacher spread0.265 · 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

Citations88
Published2020
Admission routes1
Has abstractyes

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