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Record W2145588251 · doi:10.14718/acp.2014.17.1.9

Afectaciones psicológicas de niños y adolescentes expuestos al conflicto armado en una zona rural de Colombia.

2014· article· es· W2145588251 on OpenAlexaff
Nohelia Hewitt Ramírez, Carlos Gantiva, Anderssen Vera Maldonado, Mónica Paulina Cuervo Rodríguez, Nelly Liliam Hernández Olaya, Fernando Juárez, Arturo José Parada Baños

Bibliographic record

VenueActa Colombiana de Psicología · 2014
Typearticle
Languagees
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsImpact
Fundersnot available
KeywordsHumanitiesPsychologyArt

Abstract

fetched live from OpenAlex

Se determinaron las afectaciones psicológicas de 284 niños y adolescentes expuestos al conflicto armado en una zona rural colombiana, seleccionados mediante un muestreo aleatorio por afijación proporcional. Los instrumentos aplicados fueron: la Lista de chequeo de comportamiento infantil, el Auto-reporte de comportamientos de jóvenes, la Lista de síntomas postraumáticos, la Escala de estrategias de afrontamiento para adolescentes y la Escala de resiliencia para escolares. El 72% de la población presentó afectaciones psicológicas: el 64.4%, conductas internalizadas, el 47%, conductas externalizadas en rango clínico. El 32%, problemas somáticos; el 56%, se encontraba en riesgo de estrés postraumático, y el 93% consumía alcohol en grado moderado. La estrategia de afrontamiento más utilizada era dejar que las cosas se arreglaran solas. Se encontró una alta necesidad de atención en salud. Ser hombre constituyó un factor de riesgo de depresión, agresión y problemas sociales en los niños. A su vez, tener hasta doce años y estar cursando un grado escolar bajo, lo fue para los síntomas somáticos en adolescentes. Los resultados evidenciaron la afectación en la salud mental de los participantes.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.366
Threshold uncertainty score0.728

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.012
GPT teacher head0.315
Teacher spread0.303 · 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

Citations54
Published2014
Admission routes1
Has abstractyes

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