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Record W2151015408 · doi:10.1176/appi.ps.56.11.1444

Brief Reports: The Fidelity of Supported Employment Implementation in Canada and the United States

2005· article· en· W2151015408 on OpenAlexafffundabout
Marc Corbière, Gary R. Bond, Elliot M. Goldner, Tasha Ptasinski

Bibliographic record

VenuePsychiatric Services · 2005
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of British Columbia
FundersHealth CanadaCanadian Psychiatric Research Foundation
KeywordsSupported employmentFidelityScale (ratio)Vocational educationMental illnessPsychologyPolitical scienceMental healthPsychiatryGeographyComputer scienceEngineeringPedagogyWork (physics)

Abstract

fetched live from OpenAlex

Supported employment has been documented in the United States as an evidence-based practice that helps people with severe mental illness obtain and maintain employment. The evidence is strongest for the programs that follow the individual placement and support model. This brief report examines the degree to which supported employment programs in British Columbia, Canada, are similar to those in the United States. Data from the Quality of Supported Employment Implementation Scale were compiled in 2003 for ten supported employment programs from vocational agencies in British Columbia and were compared with data from 106 supported employment programs and 38 non-supported employment programs in the United States. Overall, the Canadian supported employment programs that followed the individual placement and support model had the highest fidelity.

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)0.010
metaresearch head score (Gemma)0.068
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 score0.035
Threshold uncertainty score0.256

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.068
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.009
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.296
Teacher spread0.286 · 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

Citations37
Published2005
Admission routes3
Has abstractyes

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