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Uso de drogas en estudiantes universitarios y su relación con el maltrato durante la niñez en una universidad de San Salvador, El Salvador

2015· article· es· W2259373639 on OpenAlexaff
Eduardo Alfredo Martínez Díaz, 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
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsCentre for Global Health ResearchPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsHumanitiesPsychological distressPsychologyPolitical scienceArtPsychiatry

Abstract

fetched live from OpenAlex

El objetivo del estudio fue examinar la relación entre el uso de drogas en estudiantes universitarios de una universidad en San Salvador y su relación con el maltrato durante la niñez. Este estudio fue de corte transversal, siendo el tamaño de la muestra de 272 estudiantes, con un error muestral del 5%. Los resultados más importantes fueron: el 6.6% de los estudiantes manifestó haber sufrido abuso sexual, el 24.6% abuso físico y el mismo porcentaje fue reportado para el abuso verbal mientras que el 12.9% reportó negligencia emocional o física. El 55.1% reportó distress psicológico mínimo. El 43% ha consumido drogas alguna vez en su vida. El 58.45% tiene amigos que usan drogas. El alcohol, el cannabis y el tabaco son las drogas más usadas. El 70% de los estudiantes que usan drogas sufrieron algún tipo de maltrato. En cuanto a las asociaciones, sólo en el abuso físico se encontró una asociación estadísticamente significativa con una probabilidad de error menor al 0.05. Los datos no pueden ser generalizados a los estudiantes universitarios de San Salvador, El Salvador.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.127
GPT teacher head0.403
Teacher spread0.276 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2015
Admission routes1
Has abstractyes

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