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Age Demographics Influences Cannabis Use Pre and Post Federal Legalization in Canada and Implications for Clinical Trial Application in Healthy Populations

2020· article· en· W3017323202 on OpenAlexaboutno aff
Alison C. McDonald, Erin D. Lewis, Rong Luo, Abdul Malik Sulley, Malkanthi Evans

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsLegalizationCannabisDemographyDemographicsMedicineQuality of life (healthcare)GerontologyEnvironmental healthPsychologyPsychiatrySociology

Abstract

fetched live from OpenAlex

Introduction Cannabis has been used effectively as a medical drug for many years. With the recent federal legalization of cannabis in Canada and hemp in the United States, there is growing consumer interest in how cannabinoids can be efficacious in improving quality of life. The objective of this study was to examine how age influences cannabis use in Southwestern Ontario before and after federal legalization in Canada. Methods A 31 question, online survey was conducted from March 2018 to October 2019 and designed as a market research project to engage with Canadians on past and present medical and recreational cannabis use. The data were used to investigate how age influences self‐reported reasons for use, life stage when started, and method, frequency and component of cannabis most frequently used. Respondents were stratified into age groups (<19 (n=199), 19–24 (n=1182), 25–34 (n=1362), 35–44 (n=1166), 45–54 (n=569), 55–64 (n=537), 65+ (n=82)). Possible differences in cannabis consumption between groups pre and post federal legalization was assessed by the Chi Square test or Fisher’s Exact (2‐tail) test, as appropriate Results There were 2,667 and 2,430 survey respondents’ pre and post‐legalization, respectively. There were significant effects of age group on the cannabinoid consumed, the primary reason for using cannabis, the life stage when started using cannabis, and both the frequency and mode of consumption (p<0.01). There was a demographic shift in those who reported using cannabis for “social/relaxation” purposes following legalization. Prior to legalization, social/relaxation was the most frequently reported reason for use in those <19–24 years. Reducing anxiety was the most frequently reported reason for use for users aged 25–34 and controlling pain was most frequent amongst users aged 35–65+. Following legalization, social/relaxation became the most frequently reported reason for use across a greater age range of <19–45 years. Across all age groups, since legalization, 18–68% reported using cannabis to reduce anxiety, 23–60% to control pain, and 4–27% for digestion/ISB/IBD. These are important findings for consumers of translational research in these areas. Daily users across groups was similar before and after legalization. Prior to legalization, 40–73% reported daily use compared to 48–72% post‐legalization. There were significant differences in use of inhalation (vapor, smoke) and ingestion (oral, sublingual) routes of administration between age groups. Across groups, the most frequently reported route of administration was smoking. There were no differences in suppository (0–9%) and topical (0–18%) use. Conclusion Age influences how and why cannabis is consumed, both before and after federal legalization. Most clinical trials on cannabis have not been completed in healthy populations, which confounds the literature available to inform formulation of investigational products for trails in this population. Moving forward with effective clinical trial design for efficacious use in healthy populations requires an understanding of the needs, perceptions and barriers of consumers, making surveys such as this critical in advancing this choice of treatment modality.

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)0.014
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.263

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.055
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.102
GPT teacher head0.402
Teacher spread0.300 · 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 designObservational
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

Citations0
Published2020
Admission routes1
Has abstractyes

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