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Malestar psicológico, disfunción familiar, maltrato de estudiantes durante la niñez en una universidad privada de Bogotá, Colombia

2015· article· es· W2174356497 on OpenAlexafffund
Juan Daniel Gómez, 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
FieldPsychology
TopicStress and Burnout Research
Canadian institutionsCentre for Global Health ResearchPublic Health OntarioUniversity of Toronto
FundersUniversity of TorontoDepartamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)
KeywordsPsychologyHumanitiesArt

Abstract

fetched live from OpenAlex

Estudio derivado de una investigación exploratoria, correlacional-descriptiva, que estudió retrospectivamente el maltrato durante la infancia y su posible relación con el uso de sustancias psicoactivantes entre estudiantes universitarios. El maltrato infantil auto-reportado se evaluó mediante el Cuestionario de Experiencias Adversas Durante la Niñez, y para evaluar la disfunción familiar y el malestar psicológico (distress) se aplicó la Escala de Kessler (K10) a 302 estudiantes. Los principales indicadores de maltrato infantil fueron: negligencia =18,2%; maltrato emocional =17.9%; maltrato físico =13.6%; abuso sexual =2.0%. Otros indicadores relevantes fueron: madre/cuidadora agredida, 9.3%; madre/cuidadora golpeada repetidamente por al menos algunos minutos, 5.3%; y madre/cuidadora herida con arma blanca o de fuego, 3.6%. Se concluye que existe relación entre el "abuso emocional y el "abuso físico", y entre el "abuso emocional" y la "disfunción familiar" así como la detección de violencia de género en familias según estrato socioeconómico.

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 score0.105
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.323
GPT teacher head0.489
Teacher spread0.166 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

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