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Record W3109590665 · doi:10.1089/can.2020.0093

Use and Perceptions of Cannabidiol Products in Canada and in the United States

2020· article· en· W3109590665 on OpenAlexafffundabout
Samantha Goodman, Elle Wadsworth, Gillian L. Schauer, David Hammond

Bibliographic record

VenueCannabis and Cannabinoid Research · 2020
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Waterloo
FundersCanadian Institutes of Health Research
KeywordsCannabidiolMedicineCannabisDepression (economics)Environmental healthPublic healthAnxietyTobacco productFamily medicinePsychiatryNursing

Abstract

fetched live from OpenAlex

Objectives: This study aimed to characterize use and perceptions of cannabidiol (CBD) products. Materials and Methods: Participants aged 16–65 years in Canada ( n =15,042) and the United States ( n =30,288) completed measures on prevalence and patterns of CBD product use and perceptions of CBD oil as part of the 2019 International Cannabis Policy Study online survey. Results: Past 12-month CBD product use was significantly more prevalent among respondents in the United States (26.1%) than in Canada (16.2%). Consumers in the United States and Canada reported using a range of CBD products, including drops (46.3% vs. 47.3%, respectively), topicals (26.0% vs. 16.7%), edibles/foods (23.8% vs. 17.6%), vape oils (18.9% vs. 13.3%), capsules (13.3% vs. 16.7%), and dried flower (10.1% vs. 16.1%). CBD was most commonly reported for management of pain, anxiety, and depression. Over half of CBD consumers in both countries reported that CBD oil was beneficial for health. Conclusions: Use of CBD products is common in both the United States and Canada, primarily to manage self-reported health conditions for which there is little or no evidence of efficacy. Clearer public health messaging regarding the therapeutic effects of CBD is warranted.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.282
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
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 head0.056
GPT teacher head0.318
Teacher spread0.262 · 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 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

Citations110
Published2020
Admission routes3
Has abstractyes

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