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Is Vancouver Canada's supervised injection facility cost‐saving?

2010· article· en· W1931184024 on OpenAlexaboutno aff
Steven D. Pinkerton

Bibliographic record

VenueAddiction · 2010
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsnot available
FundersNational Institute on Drug AbuseNational Institute of Mental HealthNational Institutes of Health
KeywordsSyringeHuman immunodeficiency virus (HIV)MedicineEnvironmental healthBusinessPsychiatryFamily medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine whether Vancouver's Insite supervised injection facility and syringe exchange programs are cost-saving--that is, are the savings due to averted HIV-related medical care costs sufficient to offset Insite's operating costs? METHODS: The analyses examined the impact of Insite's programs for a single year. Mathematical models were used to calculate the number of additional HIV infections that would be expected if Insite were closed. The life-time HIV-related medical costs associated with these additional infections were compared to the annual operating costs of the Insite facility. RESULTS: If Insite were closed, the annual number of incident HIV infections among Vancouver IDU would be expected to increase from 179.3 to 262.8. These 83.5 preventable infections are associated with $17.6 million (Canadian) in life-time HIV-related medical care costs, greatly exceeding Insite's operating costs, which are approximately $3 million per year. CONCLUSIONS: Insite's safe injection facility and syringe exchange program substantially reduce the incidence of HIV infection within Vancouver's IDU community. The associated savings in averted HIV-related medical care costs are more than sufficient to offset Insite's operating costs.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.059
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.022
GPT teacher head0.287
Teacher spread0.265 · 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

Citations73
Published2010
Admission routes1
Has abstractyes

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