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Record W4224440216 · doi:10.1016/j.abrep.2022.100425

Cognitions mediate the influence of personality on adolescent cannabis use initiation

2022· article· en· W4224440216 on OpenAlexafffund
Maya A. Pilin, Jill M. Robinson, Katie Young, Marvin D. Krank

Bibliographic record

VenueAddictive Behaviors Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsPsychologyPersonalityCannabisMediationExpectancy theoryContext (archaeology)Structural equation modelingClinical psychologyDevelopmental psychologyBig Five personality traitsCognitionSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Aims: Much research indicates that an individual's personality impacts the initiation and escalation of substance use and problems in youth. The acquired-preparedness model suggests that personality influences substance use by modifying learning about substances, which then affects substance use. The current study used longitudinal data to test whether automatic cannabis-related cognitions (memory associations and outcome expectancy liking) mediate the relationship between four personality traits with later cannabis use. Methods: = 670). Results: A structural equation model supported a full mediation effect and the hypothesis that personality affects cannabis use in youth by influencing automatic memory associations and outcome expectancy liking. Further findings from the same model also indicated a mediation effect of these cognitions in the relationship between age and cannabis use. Conclusion: The findings of the study support the acquired-preparedness model where personality influences automatic associations in the context of dual-processing theories of substance 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)0.000
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 score0.001
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.035
GPT teacher head0.300
Teacher spread0.265 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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