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Record W4290839666 · doi:10.31234/osf.io/f754m

Understanding lower-risk cannabis consumption from the consumers' perspective: A rapid evidence assessment

2022· preprint· en· W4290839666 on OpenAlexaff
Renee St‐Jean, Mackenzie Dowson, Anna Stefaniak, Melissa Salmon, Nassim Tabri, Richard J. Wood, Michael J. A. Wohl

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsCarleton University
Fundersnot available
KeywordsCannabisConsumption (sociology)PsychologyNormalization (sociology)Perspective (graphical)PsychiatrySociology

Abstract

fetched live from OpenAlex

In the current rapid evidence assessment, we summarize the existing research on lower-risk cannabis consumption as understood by those who consume cannabis. We identified 7111 unique articles published between 1900 and 2021 using search terms related to a) cannabis consumption, b) beliefs and behaviors, and c) positive outcomes. Twelve articles met our inclusion criteria. Three themes emerged that reflect lower-risk cannabis beliefs and behaviors (informed self-regulation, protective behavioral strategies, and the normalization of cannabis consumption) and one theme reflected motivations that undermine lower-risk cannabis consumption (e.g., using cannabis to cope). Results suggest a need for targeted lower-risk cannabis consumption research—research focused on how those who consume cannabis do so in a positive, non-problematic manner. Such research would help to inform policy and practice and, ultimately, help promote lower-risk cannabis consumption strategies.

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)0.045
metaresearch head score (Gemma)0.197
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 score0.045
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.197
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0280.014
Science and technology studies0.0010.002
Scholarly communication0.0110.014
Open science0.0020.005
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0130.002

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 head0.167
GPT teacher head0.404
Teacher spread0.237 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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