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Record W2130191795 · doi:10.1176/appi.ps.201400359

Further Validation of the Pathways Housing First Fidelity Scale

2015· article· en· W2130191795 on OpenAlexaffabout
Paula Goering, Scott Veldhuizen, Geoffrey Nelson, Ana Stefančić, Sam Tsemberis, Carol E. Adair, Jino Distasio, Tim Aubry, Vicky Stergiopoulos, David L. Streiner

Bibliographic record

VenuePsychiatric Services · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsScale (ratio)FidelityPsychologyComputer scienceGeographyCartographyTelecommunications

Abstract

fetched live from OpenAlex

OBJECTIVE: This study examined whether Housing First fidelity ratings correspond to program operation descriptions from administrative data and predict client outcomes. METHODS: A multisite, randomized controlled trial (At Home/Chez Soi) in five Canadian cities included two assessments of 12 programs over two years. Outcomes for 1,158 clients were measured every six months. Associations between fidelity ratings and administrative data (Spearman correlations) and participant outcomes (mixed-effects modeling) were examined. RESULTS: Fidelity ratings were generally good (mean ± SD=136.6 ± 10.3 out of a possible range of 38-152; 87% of maximum value). Fidelity was significantly associated with three of four measures of program operation, with correlations between .55 and .60. Greater program fidelity was associated with improvement in housing stability, community functioning, and quality of life. CONCLUSIONS: Variation in program fidelity was associated with operations and outcomes, supporting scale validity and intervention effectiveness. These findings reinforced the value of using fidelity monitoring to conduct quality assurance and technical assistance activities.

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)0.027
metaresearch head score (Gemma)0.046
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 score0.110
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.057
GPT teacher head0.364
Teacher spread0.306 · 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

Citations42
Published2015
Admission routes2
Has abstractyes

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