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Record W2028909567 · doi:10.1177/002204260303300306

The Sydney Medically Supervised Injecting Centre: Client Characteristics and Predictors of Frequent Attendance during the First 12 Months of Operation

2003· article· en· W2028909567 on OpenAlexaboutno aff
Jo Kimber, Margaret MacDonald, Ingrid van Beek, John Kaldor, Don Weatherburn, Helen Lapsley, Richard P. Mattick

Bibliographic record

VenueJournal of Drug Issues · 2003
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsnot available
Fundersnot available
KeywordsAttendanceQuarter (Canadian coin)HeroinMedicineService (business)Family medicineMedical emergencyPsychiatryDrugBusinessPolitical scienceGeography

Abstract

fetched live from OpenAlex

This paper describes characteristics of clients registered in the first 12 months of the Sydney Medically Supervised Injecting Centre's (MSIC) operation, as well as predictors of frequent attendance. The study is based on information collected from clients at their initial registration and subsequent service utilization. Most of the 2,719 clients were male (71%), almost half had previously experienced at least one nonfatal heroin overdose, and one quarter had accessed formal drug treatment in the previous 12 months. Characteristics associated with frequent attendance at the MSIC were reporting previous attendance at the local primary health service for injection drug users (IDU), injecting drugs other than amphetamine, reporting sex work, injecting at least daily, and injecting in a public place in the month before registration.

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)0.003
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 score0.057
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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 science0.0010.001
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 head0.015
GPT teacher head0.283
Teacher spread0.267 · 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

Citations38
Published2003
Admission routes1
Has abstractyes

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