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Record W4410832579 · doi:10.1080/15388220.2025.2509893

Do Risky Drinking and Cannabis Use Motives Mediate the Links Between Bullying Involvement and Substance Use Harms Among Emerging Adults?

2025· article· en· W4410832579 on OpenAlexaffabout
Lillea A. Hohn, Laura J. Lambe, Patricia Conrod, Allyson F. Hadwin, Matthew T. Keough, Marvin D. Krank, Kara Thompson, Sherry H. Stewart

Bibliographic record

VenueJournal of School Violence · 2025
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaYork UniversityUniversity of VictoriaUniversité de MontréalSt. Francis Xavier UniversityDalhousie University
Fundersnot available
KeywordsCannabisSubstance usePoison controlPsychologySuicide preventionInjury preventionHuman factors and ergonomicsOccupational safety and healthSubstance abuseClinical psychologyPsychiatrySocial psychologyMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Individual motivations for substance use may help to explain the link between bullying involvement and harmful substance use among emerging adults. This study investigated mediational pathways between bullying involvement (victimization and perpetration) and alcohol/cannabis use harms via risky alcohol/cannabis use motives (coping-anxiety, coping-depression, enhancement, and conformity). Data came from a cross-sectional, self-report survey administered to 1898 undergraduate students in first or second year across five post-secondary Canadian institutions. Mediation analyses indicated that risky drinking and cannabis use motives mediated links between bullying involvement and harmful use of each substance. In general, coping and conformity motives emerged as mediators for victimization-harmful use links, whereas enhancement and conformity motives emerged as mediators for perpetration-harmful alcohol use links. Results may have implications for supporting vulnerable emerging adults.

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 score0.013
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.025
GPT teacher head0.294
Teacher spread0.269 · 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
Published2025
Admission routes2
Has abstractyes

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