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Record W2802638831 · doi:10.1097/yco.0000000000000429

Decriminalization of drug use

2018· review· en· W2802638831 on OpenAlexaff
Balasingam Vicknasingam, Suresh Narayanan, Darshan Singh, Marek C. Chawarski

Bibliographic record

VenueCurrent Opinion in Psychiatry · 2018
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsCentre for Drug Research and Development
Fundersnot available
KeywordsDecriminalizationMedicineLegalizationCannabisAddictionDrugPsychiatryCriminologyPsychology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To review the literature on decriminalization of drug use from 2016 to 2017 and suggest the way forward. RECENT FINDINGS: The systematic review of the literature on decriminalization resulted in seven articles that discuss decriminalization as compared with 57 published articles on legalization. Decriminalization of drug use did not have an effect on the age of onset of drug use and the prices of drugs did not decrease after the implementation of drug decriminalization. Policy-based studies on decriminalization suggest shifting from criminal sanctions to a public health approach, which was endorsed by the United Nations (UN) that viewed drug addiction as a preventable and treatable health disorder. One study preferred decriminalization only for cannabis and cautioned against regulating cannabis like alcohol. Another study indicated that general medical practitioners in Ireland did not favour the decriminalization of cannabis. SUMMARY: Scientific evidence supporting drug addiction as a health disorder and the endorsement by the UN strengthen the case for decriminalization. However, studies reporting on the positive outcomes of decriminalization remain scarce. The evidence needs to be more widespread in order to support the case for decriminalization. Furthermore, the endorsement by the UN needs to be acted upon by individual member states.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.694
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.277
GPT teacher head0.528
Teacher spread0.251 · 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 designNot applicable
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

Citations21
Published2018
Admission routes1
Has abstractyes

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