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Record W4366487500 · doi:10.1037/adb0000930

Daily associations between cannabis use and alcohol use among people who use cannabis for both medicinal and nonmedicinal reasons: Substitution or complementarity?

2023· article· en· W4366487500 on OpenAlexafffund
Sophie G. Coelho, Christian S. Hendershot, Sergio Rueda, Jeffrey D. Wardell

Bibliographic record

VenuePsychology of Addictive Behaviors · 2023
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsCentre for Addiction and Mental HealthUniversity of TorontoYork University
FundersCanadian Institutes of Health ResearchCanadian HIV Trials Network, Canadian Institutes of Health Research
KeywordsCannabisCannabis DependenceMedicinePsycINFOEffects of cannabisEnvironmental healthPsychologyPsychiatryMEDLINECannabidiolBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: medicinal and nonmedicinal reasons. This study used ecological momentary assessment to examine this question. METHOD: = 66; 53.1% men; mean age 33 years) completed daily surveys assessing previous-day reasons for cannabis use (medicinal vs. nonmedicinal), cannabis consumption (both number of different types of cannabis used and grams of cannabis flower used), and number of standard drinks consumed. RESULTS: cannabis and alcohol. The day-level association between medicinal reasons for cannabis use and lower alcohol consumption was mediated by using fewer grams of cannabis on medicinal cannabis use days. CONCLUSIONS: Day-level cannabis-alcohol associations may be complementary rather than substitutive among people who use cannabis for both medicinal and nonmedicinal reasons, and lower (rather than greater) cannabis consumption on medicinal use days may explain the link between medicinal reasons for cannabis use and reduced alcohol use. Still, these individuals may use greater amounts of both cannabis and alcohol when using cannabis for exclusively nonmedicinal reasons. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.090
GPT teacher head0.397
Teacher spread0.307 · 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 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

Citations8
Published2023
Admission routes2
Has abstractyes

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