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Record W2102781669 · doi:10.1016/j.drugpo.2014.09.001

HIV, drugs and the legal environment

2014· article· en· W2102781669 on OpenAlexaff
Steffanie A. Strathdee, Leo Beletsky, Thomas Kerr

Bibliographic record

VenueInternational Journal of Drug Policy · 2014
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of British Columbia
FundersNational Institute on Drug Abuse
KeywordsCriminalizationHarm reductionLaw enforcementEnforcementAccountabilityPublic healthCriminal justiceHarmTransparency (behavior)Political scienceCriminologyLawBusinessPublic relationsMedicineSociologyNursing

Abstract

fetched live from OpenAlex

A large body of scientific evidence indicates that policies based solely on law enforcement without taking into account public health and human rights considerations increase the health risks of people who inject drugs (PWIDs) and their communities. Although formal laws are an important component of the legal environment supporting harm reduction, it is the enforcement of the law that affects PWIDs' behavior and attitudes most acutely. This commentary focuses primarily on drug policies and policing practices that increase PWIDs' risk of acquiring HIV and viral hepatitis, and avenues for intervention. Policy and legal reforms that promote public health over the criminalization of drug use and PWID are urgently needed. This should include alternative regulatory frameworks for illicit drug possession and use. Changing legal norms and improving law enforcement responses to drug-related harms requires partnerships that are broader than the necessary bridges between criminal justice and public health sectors. HIV prevention efforts must partner with wider initiatives that seek to improve police professionalism, accountability, and transparency and boost the rule of law. Public health and criminal justice professionals can work synergistically to shift the legal environment away from one that exacerbates HIV risks to one that promotes safe and healthy communities.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.319
Teacher spread0.306 · 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 designNot applicable
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

Citations89
Published2014
Admission routes1
Has abstractyes

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