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Record W4395048315 · doi:10.1177/19485506241245744

Chronic Cannabis Use in Everyday Life: Emotional, Motivational, and Self-Regulatory Effects of Frequently Getting High

2024· article· en· W4395048315 on OpenAlexafffund
Michael Inzlicht, Taylor Bridget Sparrow-Mungal, Gregory John Depow

Bibliographic record

VenueSocial Psychological and Personality Science · 2024
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsWomen's and Gender Studies et Recherches FéministesUniversity of Toronto
FundersSocial Sciences and Humanities Research Council
KeywordsPsychologyConscientiousnessCannabisEveryday lifeSelf-controlSocial psychologyPersonalityDevelopmental psychologyClinical psychologyExtraversion and introversionBig Five personality traitsPsychiatry

Abstract

fetched live from OpenAlex

Approximately 200 million people consume cannabis annually, with a significant proportion of them using it chronically. Using experience sampling, we describe the effects of chronically getting high on emotions, motivation, effort, and self-regulation in everyday life. We queried chronic users ( N = 260) 5 times per day over 7 days (3,701 observations) to assess immediate effects of getting high and longer term, between-person effects. Getting high was associated with more positive emotions and fewer negative emotions. Contrary to stereotypes, we observed minimal effects on motivation or objective effort willingness. However, getting high was associated with lower scores on facets of conscientiousness. Surprisingly, there was no evidence of a weed hangover. Relative to less frequent users, very frequent users exhibited more negative emotions dispositionally, but they were more motivated. They also reported less self-control and willpower. As attitudes about cannabis are changing, our findings provide a rich description of its chronic use.

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)0.000
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 score0.502
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.073
GPT teacher head0.415
Teacher spread0.343 · 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

Citations7
Published2024
Admission routes2
Has abstractyes

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