MétaCan
Menu
Back to cohort
Record W2904140596 · doi:10.1016/j.abrep.2018.100154

Substance use and impaired driving prevalence among Francophone and Anglophone postsecondary students in Western Canada

2018· article· en· W2904140596 on OpenAlexafffundabout
Ndèye Rokhaya Gueye, Monique Bohémier, Danielle de Moissac

Bibliographic record

VenueAddictive Behaviors Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversité de Saint-Boniface
FundersHealth CanadaUniversité de Saint-Boniface
KeywordsFrenchPostsecondary educationPolitical sciencePsychologyHigher educationEnvironmental healthSociologyMedicineGeography

Abstract

fetched live from OpenAlex

INTRODUCTION: Substance use and impaired driving increase risk of motor vehicle crashes and deaths. Individual, socio-economic and -cultural factors are associated with these at-risk behaviors; however, little is known if differences exist between the Anglophone majority and minority Francophone populations in Canada. This article describes prevalence of substance use, impaired driving and driving practices by postsecondary student and compares Francophones and Anglophones with respect to these behaviors. METHODS: Postsecondary students between 18 and 24 years attending a Francophone university in Western Canada completed a paper-based survey during class-time. RESULTS: Prevalence of alcohol consumption, binge drinking and marijuana use in the past month were 88.6%, 64.2% and 22.7% respectively. Francophone participants were more likely to consume more alcohol, participate in drinking games, and consume marijuana during the past month than Anglophones. They were also more likely to report impaired-driving, speeding, distracted driving and being passenger in a motor vehicle driven by an impaired driver. CONCLUSION: Awareness campaigns on campus highlighting the risks of substance use and unsafe driving practices should be strengthened and target Francophone students in linguistic minority communities.

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 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 score0.700
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.261
Teacher spread0.250 · 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

Citations12
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueAddictive Behaviors ReportsSame topicSubstance Abuse Treatment and OutcomesFrench-language works237,207