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PERCEPTION OF HARM AND BENEFITS OF CANNABIS USE AMONG ADOLESCENTS FROM LATIN AMERICA AND CARIBE

2019· article· en· W2968275216 on OpenAlexaff
Maria Inês Gandolfo Conceição, María Fernanda Reyes, Patricia Henríquez, Narsha Modeste, Jason Wynter, Gaile Gray‐Phillip, Guarionex Gomez Tavarez, Danladi Chiroma Husaini, María Morgado, Karina Rivera Fierro, Hayley A. Hamilton, Akwatu Khenti, Marya Hynes, Carla Aparecida Arena Ventura, Bruna Brands

Bibliographic record

VenueTexto & Contexto - Enfermagem · 2019
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsCannabisLatin AmericansHarmPerceptionContext (archaeology)Affect (linguistics)Risk perceptionPsychologyMedicineEnvironmental healthGeographyDemographyPsychiatrySocial psychologyPolitical scienceSociology

Abstract

fetched live from OpenAlex

ABSTRACT Objective: to investigate the perception of harms and benefits associated with cannabis use among adolescents and how regulatory changes might affect their intention to use marijuana. Method: this multi-centric cross-sectional survey study. participants included 2717 students aged 15-17 from 10 cities in Belize, Brazil, Chile, Colombia, Dominican Republic, Jamaica, Mexico, St. Kitts and Nevis, and Trinidad and Tobago. Results: an average lifetime prevalence of cannabis use of 30.6% (25.8% past year, 15.8% past 30 days). Most participants reported that their closest friends use cannabis (60%); many (55%) stated that they would not use marijuana, even if it were legally available. Conclusion: statistics revealed that a strong perception of benefits, a low perception of risk, and friends’ use of cannabis were associated with individual use as well as intention to use within a hypothetical context of regulatory change.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.170
GPT teacher head0.394
Teacher spread0.224 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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