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Record W1903257424 · doi:10.48336/ijbdsb8178

Understanding Community Integration in a Housing-First Approach: Toronto At Home/Chez Soi Community-Based Research

2013· article· en· W1903257424 on OpenAlexaffabout
Linda Coltman, Susan Gapka, Dawnmarie Harriott, Michael Koo, Jenna Reid, Alex Zsager

Bibliographic record

VenueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland) · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsYork University
Fundersnot available
KeywordsCaucusMental healthEmpowermentNursingHousing FirstPublic relationsMedical educationSociologyPsychologyMedicineMental illnessPolitical science

Abstract

fetched live from OpenAlex

The Mental Health Commission of Canada’s At Home/Chez Soi project has taken a housing-first approach, providing approximately half of the project participants with housing as well as services that are tailored to meet their needs, while the other half have access to the regular supports that are available in their community. At Home/Chez Soi worked specifically with people with experiences of homelessness and mental health. The People With Lived Experience Caucus is linked to the Toronto site of the At Home/Chez Soi project and provides to all aspects of the larger project the perspective and advice of people who have experienced homelessness and used the mental health system. Given the opportunity to develop their own research project, the People With Lived Experience Caucus Research Subcommittee analyzed purposively sampled 18-month follow-up interviews from the At Home/Chez Soi Toronto evaluation in order to explore how the participants discuss and experience community integration in their day-to-day lives. Through our research we found that community integration is a complicated and non-linear process that is positively impacted by working toward the self-determination, independence, and empowerment of the project participants.

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)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.209
GPT teacher head0.374
Teacher spread0.165 · 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

Citations17
Published2013
Admission routes2
Has abstractyes

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