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Record W3150313793 · doi:10.1177/0002716220987220

Housing First and Severe Mental Disorders: The Challenge of Exiting Homelessness

2021· article· en· W3150313793 on OpenAlexfundaboutno aff
James Lachaud, Cília Mejía-Lancheros, Rosane Nisenbaum, Vicky Stergiopoulos, Patricia O’Campo, Stephen W. Hwang

Bibliographic record

VenueThe Annals of the American Academy of Political and Social Science · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term Care
KeywordsHousing FirstIntervention (counseling)Mood disordersPsychiatryRandomized controlled trialMoodSubsidyPsychologyMedicineClinical psychologyMental healthMental illnessAnxietyPolitical science

Abstract

fetched live from OpenAlex

We examine the long-term housing trajectories of 543 program participants at the Toronto site of the At Home/Chez-Soi project, a randomized controlled trial of a Housing First (HF) intervention for adults with mental disorders. The average follow-up period for our study was 5.5 years. We find that the HF approach, which includes housing subsidies and support services, was strongly associated with rapid transitions to sustained housing (70.4 percent of HF participants vs. 27.9 percent of treatment as usual participants). Mood disorders with psychotic features and primary psychotic disorders were negatively associated with the rapid and sustained housing trajectory, and alcohol use disorders were positively associated with a rapid then declining housing trajectory. We argue that to understand the long-term impacts of housing programs, research needs to better explore comprehensive and personalized care to support individuals with severe mental disorders.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.120
GPT teacher head0.457
Teacher spread0.337 · 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 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

Citations13
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueThe Annals of the American Academy of Political and Social ScienceSame topicHomelessness and Social IssuesFrench-language works237,207