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Record W2143350750 · doi:10.15171/ijhpm.2015.73

Examining the potential role of a supervised injection facility in Saskatoon, Saskatchewan, to avert HIV among people who inject drugs

2015· article· en· W2143350750 on OpenAlexaffabout
Ehsan Jozaghi, Asheka Jackson

Bibliographic record

VenueInternational Journal of Health Policy and Management · 2015
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDowntownHuman immunodeficiency virus (HIV)Injection drug useMedicineBusinessEnvironmental healthFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Research predicting the public health and fiscal impact of Supervised Injection Facilities (SIFs), across different cities in Canada, has reported positive results on the reduction of HIV cases among People Who Inject Drugs (PWID). Most of the existing studies have focused on the outcomes of Insite, located in the Vancouver Downtown Eastside (DTES). Previous attention has not been afforded to other affected areas of Canada. The current study seeks to address this deficiency by assessing the cost-effectiveness of opening a SIF in Saskatoon, Saskatchewan. METHODS: We used two different mathematical models commonly used in the literature, including sensitivity analyses, to estimate the number of HIV infections averted due to the establishment of a SIF in the city of Saskatoon, Saskatchewan. RESULTS: Based on cumulative cost-effectiveness results, SIF establishment is cost-effective. The benefit to cost ratio was conservatively estimated to be 1.35 for the first two potential facilities. The study relied on 34% and 14% needle sharing rates for sensitivity analyses. The result for both sensitivity analyses and the base line estimates indicated positive prospects for the establishment of a SIF in Saskatoon. CONCLUSION: The opening of a SIF in Saskatoon, Saskatchewan is financially prudent in the reduction of tax payers' expenses and averting HIV infection rates among PWID.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.354
Teacher spread0.315 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations25
Published2015
Admission routes2
Has abstractyes

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