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Record W2950892886 · doi:10.1097/coh.0000000000000566

Transforming lives and empowering communities

2019· review· en· W2950892886 on OpenAlexaff
Mat Southwell, Shaun Shelly, Virginia Macdonald, Annette Verster, Lisa Maher

Bibliographic record

VenueCurrent Opinion in HIV and AIDS · 2019
Typereview
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsInstitute of Infection and Immunity
FundersWorld Health Organization
KeywordsHarm reductionPsychological interventionDisinvestmentMedicineIntervention (counseling)WastingBusinessHarmEconomic growthIncentiveEnvironmental healthHuman immunodeficiency virus (HIV)Political scienceNursingEconomicsFamily medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: We reviewed the global state of harm reduction for people who use and/or inject drugs. KEY FINDINGS: Although harm reduction is now the key response to HIV among people who use drugs globally, intervention coverage remains suboptimal, exacerbated by chronic under-funding, declining donor support and limited domestic investment, particularly in low-income and middle-income countries. We describe the current environment and review recent innovations and responses, including peer distribution of naloxone, low dead space syringes, drug consumption rooms and drug-checking services. However, despite efforts by people who use drugs and supporting partners to sustain harm reduction services and to develop and implement novel interventions, programmes are often under-scaled and under-resourced and people who use drugs continue to face significant barriers to accessing services. SUMMARY: There is an urgent need to bring existing harm reduction programmes to scale and to broaden their scope, as well to complement them with innovative interventions targeting new populations and new substances. Under and disinvestment in harm reduction and the absence of enabling legal environments threatens to undermine the global HIV response and exacerbate the morbidity and mortality associated with the current epidemic of opioid overdose.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.918
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.218
GPT teacher head0.456
Teacher spread0.237 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations15
Published2019
Admission routes1
Has abstractyes

Explore more

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