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Record W2033726898 · doi:10.1080/07448481.2010.502194

Social Norms of Alcohol, Smoking, and Marijuana Use Within a Canadian University Setting

2010· article· en· W2033726898 on OpenAlexaffabout
Kelly P. Arbour‐Nicitopoulos, Matthew Kwan, David Lowe, Sara Taman, Guy Faulkner

Bibliographic record

VenueJournal of American College Health · 2010
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSocial norms approachPsychologyAlcoholClinical psychologySocial psychologyPsychiatryEnvironmental healthMedicinePerception

Abstract

fetched live from OpenAlex

OBJECTIVE: to study actual and perceived substance use in Canadian university students and to compare these rates with US peers. PARTICIPANTS: students (N = 1,203) from a large Canadian university. METHODS: participants were surveyed using items from the National College Health (NCHA) Assessment of the American College Health Association questionnaire. RESULTS: alcohol was the most common substance used (65.8%), followed by marijuana (13.5%) and cigarettes (13.5%). Substance use and norms were significantly less than the NCHA US data. Overall, respondents generally perceived the typical Canadian student to have used all 3 substances. Perceived norms significantly predicted use, with students more likely to use alcohol, cigarettes, or marijuana if they perceived the typical student to use these substances. CONCLUSIONS: similar to their US peers, Canadian university students have inaccurate perceptions of peer substance use. These misperceptions may have potentially negative influences on actual substance use and could be a target for intervention. Further research examining the cross-cultural differences for substance abuse is warranted.

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 score0.158
Threshold uncertainty score0.978

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 head0.022
GPT teacher head0.293
Teacher spread0.271 · 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

Citations121
Published2010
Admission routes2
Has abstractyes

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