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Record W4407900489 · doi:10.1177/10780874251314838

Housing First for Youth Who Experience Homelessness: A Systematic Review

2025· review· en· W4407900489 on OpenAlexafffund
Julia Woodhall‐Melnik, Cassandra Monette, Chloé Reiser, Tobin LeBlanc Haley

Bibliographic record

VenueUrban Affairs Review · 2025
Typereview
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of New Brunswick
FundersSocial Sciences and Humanities Research Council of CanadaCanada Research Chairs
KeywordsHousing FirstVariety (cybernetics)Thematic analysisSystematic reviewPolitical scienceCriminologySociologyPsychologyPublic relationsQualitative researchMEDLINESocial scienceMental healthLawMental illness

Abstract

fetched live from OpenAlex

Housing First programs are widely used to house adults with histories of chronic homelessness. Recently, Housing First for Youth (HF4Y) emerged as a targeted response to youth homelessness and is presently cited as the ideal model; however, researchers have yet to synthesize the evidence on which this claim is made. Through a systematic review of literature, the present authors fill this gap by offering a thematic synthesis of available peer-reviewed evidence on the impacts of HF4Y on a variety of outcomes for youth who experience homelessness. Five databases were searched from inception to April 2023 and reference lists of relevant articles were hand searched for additional studies. This search finds seven studies that specifically measure outcomes associated with HF4Y enrollment. Findings indicate that HF4Y can improve housing security, but additional research is needed to develop a more robust evidence base. There is a need for longitudinal studies, randomized controlled trials, and rich qualitative studies of HF4Y.

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)0.005
metaresearch head score (Gemma)0.021
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 score0.012
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0080.008
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.001
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 head0.101
GPT teacher head0.443
Teacher spread0.342 · 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

Citations4
Published2025
Admission routes2
Has abstractyes

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