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Record W2089303659 · doi:10.3109/10826084.2013.787096

Reducing Service and Substance Use Among Frequent Service Users: A Brief Report From the Toronto Community Addictions Team

2013· article· en· W2089303659 on OpenAlexaffabout
Jenna van Draanen, Simon Corneau, Thomas R. Henderson, Adam Quastel, Robin Griller, Vicky Stergiopoulos

Bibliographic record

VenueSubstance Use & Misuse · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of TorontoUniversité du Québec à MontréalSt. Michael's Hospital
Fundersnot available
KeywordsThematic analysisHarm reductionAddictionPsychologySubstance usePopulationIntervention (counseling)Qualitative researchService (business)MedicineClinical psychologyPsychiatryNursingPublic healthEnvironmental health

Abstract

fetched live from OpenAlex

The Toronto Community Addictions Team (TCAT) is an intensive case management intervention designed to serve people with addictions who are frequent service users, thus addressing a health system priority. Questionnaires given to 65 participants at baseline, 3 months, and 6 months and semi-structured interviews of 10 program participants explored participants' outcomes and experiences with the program. Qualitative findings, analyzed using thematic content analysis, suggest that participants value the program's commitment to harm reduction, financial trusteeship, and recovery orientation. Quantitative findings from paired t-tests reveal that participants improved in community functioning and decreased days of problematic substance use and money spent on alcohol and drugs as early as 3 months after program participation. Future research should used a controlled design and explore predictors of positive outcomes in this vulnerable population.

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)0.003
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.817

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.352
GPT teacher head0.506
Teacher spread0.154 · 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

Citations13
Published2013
Admission routes2
Has abstractyes

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