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Record W4401267303 · doi:10.22158/grhe.v7n3p1

Risk Factors for Cannabis Use-Related Consequences in Emerging Adults

2024· article· en· W4401267303 on OpenAlexaffabout
Tamara Samardzic, Berlyn Soulliere, Sanya Sagar, Carlin J. Miller

Bibliographic record

VenueGlobal Research in Higher Education · 2024
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsMcMaster UniversityUniversity of WindsorUniversity of Alberta
Fundersnot available
KeywordsCannabisConscientiousnessBig Five personality traitsPsychologyPersonalityClinical psychologyRecreationAgreeablenessPsychiatryMedicineEnvironmental healthExtraversion and introversionSocial psychology

Abstract

fetched live from OpenAlex

Cannabis use in Canada is a public health concern, especially it has been legalized for medical and recreational use. Although widely used, it appears there are negative consequences for some, but not all, cannabis consumers, and it remains challenging to predict which individuals will experience difficulties. This study examines the role of several risk factors in predicting negative outcomes of cannabis use in university students with a particular focus on personality and mental health variables. Undergraduate students (N=370) enrolled in a southwestern Ontario university participated in an online study. Preliminary analyses suggested that higher stress and lower conscientiousness were associated with cannabis use. In follow-up analyses, perceived stress was significantly associated with functional consequences of cannabis use when the quantity of cannabis used was accounted for in the analyses. Agreeableness and conscientiousness were significantly associated with overall GPA after controlling for amount of cannabis used. Individuals using low-to-moderate levels of cannabis had fewer functional consequences than individuals using higher amounts of cannabis, but these groups could not be differentiated in terms of their overall GPA. These results address a niche in the field of cannabis research in higher education and have significant implications in terms of policy and clinical practice.

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 score0.231
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
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 head0.088
GPT teacher head0.445
Teacher spread0.357 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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