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Record W4399730397 · doi:10.1186/s13011-024-00615-9

Understanding youth and young adult cannabis use in Canada post-legalization: a scoping review on a public health issue

2024· review· en· W4399730397 on OpenAlexafffundabout
Toula Kourgiantakis, Ragave Vicknarajah, Judith Logan, Travonne Edwards, Eunjung Lee, Shelley L. Craig, Ashima Kaura, Charmaine C. Williams, Savannah Marshall

Bibliographic record

VenueSubstance Abuse Treatment Prevention and Policy · 2024
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsToronto Metropolitan UniversityUniversity of TorontoUniversité Laval
FundersUniversity of TorontoRoyal Bank of Canada
KeywordsCannabisPsycINFOCINAHLMedicineYoung adultPsychiatryRecreationLegalizationMEDLINEFamily medicineGerontologyPolitical sciencePsychological intervention

Abstract

fetched live from OpenAlex

BACKGROUND: Canada legalized recreational cannabis in 2018, and one of the primary objectives of the Cannabis Act was to protect youth by reducing their access to cannabis and providing public education. Canada has the highest prevalence of cannabis use worldwide, particularly among youth and young adults under the age of 25. Cannabis use is linked with many adverse effects for youth and young adults including psychosis, anxiety, depression, respiratory distress, cannabinoid hyperemesis syndrome, and impaired cognitive performance. Despite the high prevalence of cannabis use and the evolution of policies in Canada and globally, significant knowledge and research gaps remain regarding youth and young adult cannabis use. The aim of this scoping review is to map the extent, nature, and range of evidence available on youth and young adult cannabis use in Canada since its legalization, in order to strengthen policies, services, treatments, training, and public education strategies. METHODS: Using a scoping review framework developed by Arksey and O'Malley, along with the PRISMA-ScR guidelines, we conducted a rigorous search in five academic databases: MEDLINE, Embase, APA PsycINFO, CINAHL and Web of Science Core Collection. We included empirical studies that collected data in Canada after the legalization of recreational cannabis (October 2018) and focused on youth or young adults < 30. Two reviewers independently screened articles in two stages and extracted relevant information from articles meeting the inclusion criteria. RESULTS: Of the 47 articles meeting our inclusion criteria, 92% used quantitative methods, 6% were qualitative, and 2% used a mixed-methods approach. Over two-thirds (68%) used secondary data. These studies were categorized into six focus areas: (1) prevalence, patterns, and trends, (2) cannabis-related injuries and emergency department (ED) visits, (3) rates and patterns during the pandemic, (4) perceptions of cannabis use, (5) prevention tools, and (6) cannabis-related offenses. Key findings from the studies reviewed include an increase in cannabis use among 18-24-year-olds post-legalization, with mixed results for youth under 18. ED visits for intentional and unintentional cannabis-related injuries have increased in young children and teens. Perception studies show a mix of concern and normalization of cannabis use. Though limited, prevention studies are promising in raising awareness. A decline in cannabis-related offenses was noted by one study. The review highlights several research gaps, including the need for more qualitative data, disaggregation of demographic data, intervention research, and comprehensive studies on the physical and mental health impacts of cannabis use among youth and young adults. CONCLUSION: Maintaining a public health approach is critical, with a focus on reducing the high prevalence of cannabis use among youth and young adults. This involves implementing prevention strategies to minimize harms, enhancing public education, minimizing commercialization, reducing youth access to cannabis, promoting guidelines for lower-risk cannabis use and harm reduction strategies, and increasing training for healthcare providers.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.549
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
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 head0.187
GPT teacher head0.413
Teacher spread0.226 · 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 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

Citations23
Published2024
Admission routes3
Has abstractyes

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