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Record W2166747936 · doi:10.1007/s10464-015-9709-z

Implementing Housing First Across Sites and Over Time: Later Fidelity and Implementation Evaluation of a Pan‐Canadian Multi‐site Housing First Program for Homeless People with Mental Illness

2015· article· en· W2166747936 on OpenAlex
Eric Macnaughton, Ana Stefančić, Geoffrey Nelson, Rachel Caplan, Greg Townley, Tim Aubry, Scott McCullough, Michelle Patterson, Vicky Stergiopoulos, Catherine Vallée, Sam Tsemberis, Marie‐Josée Fleury, Myra Piat, Paula Goering

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAmerican Journal of Community Psychology · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCentre for Addiction and Mental HealthUniversity of OttawaSimon Fraser UniversityDouglas CollegeMcGill UniversityDouglas Mental Health University InstituteUniversité LavalWilfrid Laurier UniversityUniversity of WinnipegCentre de Santé et de Services Sociaux de la Vieille-CapitaleCentre for Global Health ResearchUniversity of Toronto
Fundersnot available
KeywordsFidelityFocus groupHousing FirstMental illnessMental healthVocational educationQualitative researchHealth psychologyPublic housingPsychologySupportive housingProgram evaluationMedical educationPublic healthPublic relationsNursingSociologyMedicinePedagogyBusinessPolitical sciencePsychiatryEngineeringPublic administrationMarketing

Abstract

fetched live from OpenAlex

This article examines later fidelity and implementation of a five-site pan-Canadian Housing First research demonstration project. The average fidelity score across five Housing First domains and 10 programs was high in the first year of operation (3.47/4) and higher in the third year of operation (3.62/4). Qualitative interviews (36 key informant interviews and 17 focus groups) revealed that staff expertise, partnerships with other services, and leadership facilitated implementation, while staff turnover, rehousing participants, participant isolation, and limited vocational/educational supports impeded implementation. The findings shed light on important implementation "drivers" at the staff, program, and community levels.

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.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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 integrity0.0000.000
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 head0.093
GPT teacher head0.507
Teacher spread0.414 · 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