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Record W4395011450 · doi:10.1080/10530789.2024.2342049

Lifetime criminal justice involvement is not a barrier to Housing First effectiveness and cost-effectiveness

2024· article· en· W4395011450 on OpenAlexafffundabout
Marichelle Leclair, Anne G. Crocker, Ashley J. Lemieux, Laurence Roy, Tonia L. Nicholls, Zhirong Cao, Éric Latimer

Bibliographic record

VenueJournal of Social Distress and the Homeless · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsBC Mental Health & Substance Use ServicesUniversity of British ColumbiaMcGill UniversityDouglas Mental Health University InstituteUniversité de MontréalUniversité du Québec en OutaouaisInstitut national de psychiatrie légale Philippe-Pinel
FundersFonds de Recherche du Québec - Santé
KeywordsEconomic JusticeCriminal justiceCriminologyBusinessPsychologyLaw and economicsPolitical scienceLawEconomics

Abstract

fetched live from OpenAlex

Introduction: Because justice involvement of people experiencing homelessness and mental illness reduces residential stability and increases economic costs, patterns of criminal history may have an impact on the effectiveness and the cost-effectiveness of the Housing First intervention. Method. This study examined whether the effectiveness and cost-effectiveness of Housing First compared to usual services vary according to these profiles in participants recruited for the multi-site Canadian At Home/Chez Soi randomized controlled trial. Generalized linear models are used to examine the evolution of costs, days in stable housing, and net monetary benefit over two years. Results. The effectiveness or the cost-effectiveness of Housing First do not differ according to these profiles. All people who are experiencing homelessness and mental illness are likely to benefit from Housing First, regardless of criminal history. Discussion. This works provide further support for offering Housing First to all individuals expressing a desire for housing.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.048
GPT teacher head0.396
Teacher spread0.348 · 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 teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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

Citations0
Published2024
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Social Distress and the HomelessSame topicHomelessness and Social IssuesFrench-language works237,207