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PERCEPTION OF DAMAGE AND BENEFITS ASSOCIATED TO THE USE OF MARIJUANA IN ADOLESCENTS, VIÑA DEL MAR, CHILE

2019· article· en· W2967687585 on OpenAlexaff
María Morgado, Akwatu Khenti

Bibliographic record

VenueTexto & Contexto - Enfermagem · 2019
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsHarmConsumption (sociology)PerceptionContext (archaeology)Exploratory researchRisk perceptionPsychologyEnvironmental healthSample (material)MedicineSocial psychologyGeographySociologySocial science

Abstract

fetched live from OpenAlex

ABSTRACT Objective: analyze the perception of harm and benefits, and its association with the use of marijuana in high school students, as well as the intention to use it in a context of regulatory changes. Method: a quantitative, exploratory, cross-sectional study was designed, applying a self-administered questionnaire to 268 high school students. Results: The results showed that the declared consumption in the sample is higher than that obtained in previous studies in Chile, which had already warned of the increase in prevalence, compared to previous measurements. There is a low perception of risk associated with consumption and insecurity regarding benefits. In the framework of regulatory changes, no change was observed in the intention of use. Adolescent consumers would continue to do so as before, while those who have not consumed it, 25% would try it, and 60% would still not use it. Conclusions: The current discussion in the country has focused on the effect that the change in the law would have, by itself, on adolescent consumption, however, it is relevant to direct efforts towards the perceptions of risk and benefits that they have, in order to stop the observed increasing in consumption in the country, in the latest studies.

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 score0.033
Threshold uncertainty score0.066

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 head0.240
GPT teacher head0.407
Teacher spread0.167 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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