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Record W4297199856 · doi:10.1111/dar.13546

Changes in health harms due to cannabis following legalisation of non‐medical cannabis in Canada in context of cannabis commercialisation: A scoping review

2022· review· en· W4297199856 on OpenAlexafffundabout
Daniel T. Myran, Sameer Imtiaz, Lauren Konikoff, Laura Douglas, Tara Elton‐Marshall

Bibliographic record

VenueDrug and Alcohol Review · 2022
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsPublic Health OntarioUniversity of TorontoWestern UniversityCentre for Addiction and Mental HealthOttawa HospitalUniversity of Ottawa
FundersInstitute of Population and Public HealthInstitute of Neurosciences, Mental Health and AddictionCanadian Institutes of Health Research
KeywordsCannabisMedical cannabisContext (archaeology)Per capitaMedicineEnvironmental healthBusinessPsychiatryGeographyPopulation

Abstract

fetched live from OpenAlex

ISSUE: On 17 October 2018, Canada legalised non-medical cannabis. Critically, the cannabis market in Canada has changed considerably since legalisation. In this scoping review, we identified available evidence on changes in cannabis-related health harms following legalisation and contextualised findings based on legal market indicators. APPROACH: Electronic searches were conducted to identify studies that compared changes in cannabis-related health harms pre- and post-legalisation. We contextualised each study by the mean per capita legal cannabis stores and sales during the study period and compared study means to per capita stores and sales on October 2021-3 years following legalisation. IMPLICATIONS AND CONCLUSIONS: Some measures of cannabis harms have increased since legalisation but studies to date have captured periods of relatively low market maturity. Longer-term monitoring of health harms as the market continues to expand is indicated.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.465
Threshold uncertainty score0.936

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0100.014
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.071
GPT teacher head0.404
Teacher spread0.333 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations43
Published2022
Admission routes3
Has abstractyes

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