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Uso de drogas en estudiantes de medicina y su relación con experiencias de maltrato durante la infancia y adolescencia en Uruguay

2015· article· es· W2285177361 on OpenAlexaff
Miguel Pizzanelli, Robert B. Mann, Hayley A. Hamilton, Pat Erickson, Bruna Brands, Norman Giesbrecht, Maria da Glória Miotto Wright, Francisco Cumsille, Jaime Sapag, Akwatu Khenti

Bibliographic record

VenueTexto & Contexto - Enfermagem · 2015
Typearticle
Languagees
FieldSocial Sciences
TopicYouth, Drugs, and Violence
Canadian institutionsCentre for Global Health ResearchPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsHumanitiesPsychologySubstance usePhilosophyPsychiatry

Abstract

fetched live from OpenAlex

Se evaluó la prevalencia del uso y abuso de sustancias psicoactivas en los estudiantes y su relación con las experiencias adversas durante la infancia y la adolescencia en una investigación de tipo exploratorio, transversal, observacional, basada en el autoreporte de 280 estudiantes universitarios. El consumo reportado de sustancias psicoactivas fue del 72.1%. Las tres sustancias psicoactivas más frecuentemente utilizadas en el último año fueron el alcohol (24.3%), la marihuana (19.3%) y el tabaco (16.4%). Un 33.9% de los estudiantes refirieron que sus pares abusaban de sustancias. El maltrato físico y el psicológico fueron las categorías más frecuentemente reportadas. Los estudiantes que afirmaron tener pares que abusaban de sustancias psicoactivas presentaron una probabilidad siete veces mayor de abusar de drogas que el resto de los encuestados. No se encontraron asociaciones estadísticamente significativas (< 0.005) entre el reporte de maltrato en ninguna de sus categorías, y el uso o abuso de drogas.

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)0.004
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 score0.123
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.217
GPT teacher head0.464
Teacher spread0.248 · 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
Published2015
Admission routes1
Has abstractyes

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