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Record W4391896515 · doi:10.1186/s12954-023-00914-7

The war on drugs is a war on us: young people who use drugs and the fight for harm reduction in the Global South

2024· article· en· W4391896515 on OpenAlexafffund
M. J. Stowe, Rita Gatonye, Ishwor Maharjan, Seyi Kehinde, Sidarth Arya, Jorge Herrera Valderrábano, Angela McBride, Florian Scheibein, Emmy Kageha Igonya, Danya Fast

Bibliographic record

VenueHarm Reduction Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsBritish Columbia Centre on Substance Use
FundersSimon Fraser University
KeywordsHarm reductionHarmPsychological interventionHealth psychologySocial policyGlobal healthPublic relationsEconomic growthPolitical scienceMedicineCriminologyDevelopment economicsPublic healthPsychologyPsychiatryNursingEconomicsLaw

Abstract

fetched live from OpenAlex

In the Global South, young people who use drugs (YPWUD) are exposed to multiple interconnected social and health harms, with many low- and middle-income countries enforcing racist, prohibitionist-based drug policies that generate physical and structural violence. While harm reduction coverage for YPWUD is suboptimal globally, in low- and middle-income countries youth-focused harm reduction programs are particularly lacking. Those that do exist are often powerfully shaped by global health funding regimes that restrict progressive approaches and reach. In this commentary we highlight the efforts of young people, activists, allies, and organisations across some Global South settings to enact programs such as those focused on peer-to-peer information sharing and advocacy, overdose monitoring and response, and drug checking. We draw on our experiential knowledge and expertise to identify and discuss key challenges, opportunities, and recommendations for youth harm reduction movements, programs and practices in low- to middle-income countries and beyond, focusing on the need for youth-driven interventions. We conclude this commentary with several calls to action to advance harm reduction for YPWUD within and across Global South settings.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.031
GPT teacher head0.327
Teacher spread0.296 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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