MétaCan
Menu
Back to cohort
Record W2018333484 · doi:10.1081/ja-200042287

Sociodemographic Disparities in Access to Addiction Treatment Among a Cohort of Vancouver Injection Drug Users

2005· article· en· W2018333484 on OpenAlexafffundabout
Evan Wood, Kathy Li, Anita Palepu, David C. Marsh, Martin T. Schechter, Robert S. Hogg, Julio Montaner, Thomas Kerr

Bibliographic record

VenueSubstance Use & Misuse · 2005
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsVancouver Coastal HealthAIDS VancouverSt. Paul's HospitalUniversity of British Columbia
FundersNational Institute on Drug AbuseCanadian Institutes of Health Research
KeywordsAddictionMedicineCohortEthnic groupAddiction treatmentHuman immunodeficiency virus (HIV)DrugCohort studyPsychiatryDemographyFamily medicineInternal medicinePolitical science

Abstract

fetched live from OpenAlex

Vancouver's explosive HIV epidemic among injection drug users (IDUs) has received international attention due to the presence of a large needle exchange program. The role of addiction treatment has not been evaluated in this setting. We evaluated factors associated with use of addiction treatment among a prospective cohort of Vancouver IDUs. Addiction treatment was negatively associated with Aboriginal ethnicity and unstable housing, both of which have been associated with HIV infection in previous studies. These findings demonstrate low levels of addiction treatment among Vancouver IDUs and suggest that programs may need to be targeted towards specific populations with poor access.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.383
Threshold uncertainty score0.770

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.036
GPT teacher head0.325
Teacher spread0.289 · 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

Citations43
Published2005
Admission routes3
Has abstractyes

Explore more

Same venueSubstance Use & MisuseSame topicHIV, Drug Use, Sexual RiskFrench-language works237,207