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KNOWLEDGE OF THE CONSEQUENCES AND USE OF DRUGS FOR COSTA RICA UNIVERSITY STUDENTS

2019· article· en· W2958147106 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTexto & Contexto - Enfermagem · 2019
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsDrugSample (material)MedicinePsychologyEnvironmental healthDemographyPsychiatrySociology

Abstract

fetched live from OpenAlex

ABSTRACT Objective: to determine the relationship between knowledge of consequences and drug use in undergraduate students of a university in San José, Costa Rica. Method: the cross-sectional study examines the demographic profile of the sample and the relationship between knowledge of consequences, drug use and academic performance. The study focuses on three types of drugs: alcohol, marijuana and cocaine. Three variables will be analyzed: demographic data, knowledge of consequences and use of drugs. Results: the relationship between knowledge of consequences and use of drugs was made using of the T-test. The sample had 272 students, 28.2% (n=77) of them were men and 71.4% were women (n=195). They were selected from the areas of social sciences (n=137, 50.2%), and from the area of health sciences (n=136; 49.8%). Alcohol was the most used drug (n=217, 79.8%), followed by marijuana (n=72, 26.6%) and finally cocaine (n=3, 1.1%) in the last 12 months. Conclusion: the results shown indicate that there is no significant relationship between such variables. The findings are important at the level of drug policies to support the development of new preventive strategies for drug use.

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.

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 score0.066
Threshold uncertainty score0.378

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 head0.295
GPT teacher head0.435
Teacher spread0.140 · 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