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Supervised Consumption Services

2023· book-chapter· en· W4386823700 on OpenAlexaff
Jeanette M. Bowles, Meaghan Thumath, Gillian Kolla, Zoë Dodd, Jaime Arredondo Sanchez Lira, Frank Crichlow, Leo Beletsky

Bibliographic record

VenueOxford University Press eBooks · 2023
Typebook-chapter
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAuthorizationConsumption (sociology)Public healthMedicineInjection drug useHuman immunodeficiency virus (HIV)DrugTransmission (telecommunications)Environmental healthBusinessMedical emergencyComputer securityFamily medicinePsychiatryNursingDrug injectionEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Abstract Supervised consumption services (SCS) are spaces where people who use drugs can consume pre-obtained drugs under the guidance of personnel trained to mitigate drug-related health harms and provide wraparound services. First developed during the HIV/AIDS crisis, SCS have gained prominence as an instrument of preventing overdoses and bloodborne disease transmission in locales with high prevalence of public drug injection. Globally, there are now over 120 sanctioned SCS across 13 countries, operating under varying degrees of legal authorization. In addition to reducing overdose deaths and HIV transmission, SCS have been shown to prevent soft tissue infections, link clients to needed medical and social services, facilitate substance use treatment uptake, reduce public drug consumption, and reduce improperly discarded injection equipment. Despite a robust evidence base of positive health and public safety impact, SCS remain controversial and under-used.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.363
Threshold uncertainty score0.909

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 studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.3630.125

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 head0.072
GPT teacher head0.279
Teacher spread0.207 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations4
Published2023
Admission routes1
Has abstractyes

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