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[Perceived norm for marijuana use among university student peers].

2010· article· en· W1774411266 on OpenAlexaff
Angélica Cazenave, John Cunningham, Bruna Brands, Carol Strıke, Maria da Glória Miotto Wright

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicYouth, Drugs, and Violence
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNorm (philosophy)PsychologySocial norms approachRecreationHigher educationData collectionClinical psychologySocial psychologyMedicinePolitical scienceSociologySocial science

Abstract

fetched live from OpenAlex

UNLABELLED: This article describes the relationship between the perceived norm for marijuana use among peers and the use reported by university students in Chile. This is a qualitative, multicenter, cross-sectional study. A total 449 students participated with free consent. Data collection was performed using a self-administered questionnaire, and SPSS 15.0 was used for data analysis. RESULTS: most participants were women, single, between 20 and 21 years of age, students of nursing and education. The perceived norm is higher than reports from national studies (32.1% against 21%) and higher than the self-reported use (32.1% against 5.6%). Marijuana use is higher among women, in nursing and education courses. It was confirmed that marijuana use among women begins at the university; as it was used as a recreational drug and there was more tolerance among peers. It was observed that marijuana use was overestimated, which, according to the Theory of Social Norms and Normalization, has an effect on the increase of drug use. This study is a contribution for the country's and the university's policies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.263
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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
Published2010
Admission routes1
Has abstractyes

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