MétaCan
Menu
Back to cohort
Record W2326800812 · doi:10.1055/s-0031-1277825

The At Home/Chez Soi Canadian Study of Housing First for people who are homeless and mentally ill: study design and baseline data for the Montreal site

2011· article· en· W2326800812 on OpenAlexaffabout
Éric Latimer, Daniel Rabouin

Bibliographic record

VenuePsychiatrische Praxis · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsDouglas Mental Health University Institute
Fundersnot available
KeywordsAssertive community treatmentSupportive housingHousing FirstMental illnessMental healthGerontologyPsychologySubsidyBaseline (sea)Adaptation (eye)MedicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

Background/Objectives: The „Pathways to Housing“ service model for helping people who are homeless and severely mentally ill, developed in New York City, involves providing immediate access to a choice of subsidized, scattered site apartments, together with a recovery-oriented adaptation of Assertive Community Treatment. U.S. evidence suggests that this variant of the „Housing First“ approach is more effective than traditional, step-wise services at helping people to become and remain housed, while increasing perceived choice with regards to housing. It has not yet been experimentally evaluated outside the United States, however. There is even less evidence concerning how this model can be adapted to homeless persons with less severe mental illness. A CAN$110-million, 5-year experimental study of both the Pathways version of Housing First, and an Intensive Case Management (ICM) adaptation for people with less severe mental illness, has been mounted in Canada. Called „At Home/Chez Soi“, the study is being carried out simultaneously in 5 Canadian cities: Vancouver, Winnipeg, Toronto, Montreal, and Moncton. After a brief description of the genesis and organization of the study, the experimental approaches being tested in each city will be described. The common screening and psychometric instruments that form the core batteries being used in each city will be described. Implementation and sub-studies at the Montreal site will be described in more detail. Baseline data on the participant sample in Montreal will be provided. Methods: Recruitment and randomisation to experimental conditions and treatment as usual began in October 2009. By June 2011 about 2,300 participants, including 500 in Montreal, are expected to have been recruited. A common data collection protocol, involving both quantitative and qualitative measures, spanning many dimensions including recovery, with a follow-up period of 2 years, is being administered in each city. An algorithm assigns recruited participants to a high needs (Housing First + ACT) or moderate needs (Housing First + ICM) group based on their diagnoses (including concurrent substance use disorders) and functional difficulties. Results: To date, over 300 participants have been recruited in Montreal, of whom 71% are male, with an average age of 44 years. Baseline data and correlational analyses on the entire sample will be presented, spanning the following domains: sociodemographics, housing history, Axis I mental health diagnoses, substance use disorders, symptoms (Colorado Symptom Index), functioning (Multnomah Community Ability Scale), physical and mental health (SF-12), health-related quality of life (EQ-5D and SF-6D), recovery (Recovery Assessment Scale), employment status, income, and health and justice service use. Discussion/Conclusions: The At Home/Chez Soi study is an ambitious study of Housing First approaches for homeless people with mental illness. It is currently proceeding as planned. Challenges associated with implementation will be discussed. Funding: Mental Health Commission of Canada.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.360

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0110.002
Scholarly communication0.0020.001
Open science0.0040.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.088
GPT teacher head0.366
Teacher spread0.278 · 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 designObservational
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
Published2011
Admission routes2
Has abstractyes

Explore more

Same venuePsychiatrische PraxisSame topicHomelessness and Social IssuesFrench-language works237,207