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Record W2808438239 · doi:10.36829/63cts.v4i2.497

Depresión y ansiedad en adolescentes de Santa Rosa, Guatemala

2017· article· es· W2808438239 on OpenAlexaff
Isabel A. Arreaga, Pablo E. Galindo, Alma R. Alfaro, M. Hernández Martínez, Mario G. Pivaral, Pablo B. Cho

Bibliographic record

VenueCiencia Tecnologí­a y Salud · 2017
Typearticle
Languagees
FieldPsychology
TopicStress and Burnout Research
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsHumanitiesDepression (economics)PsychologyMedicinePsychiatryArt

Abstract

fetched live from OpenAlex

Los transtornos mentales y neurológicos representan el 14% de las enfermedades a nivel mundial y el 22% en Latinoamérica, siendo las más comunes depresión (5%) y ansiedad (3.4%). En Guatemala durante el 2015 se reportó un incremento del 40% de los trastornos mentales, encabezados por depresión y ansiedad. Con el objetivo de estimar la prevalencia de depresión y ansiedad en adolescentes de institutos nacionales de educación básica y diversificada, se realizó un estudio descriptivo transversal. Se seleccionaron tres municipios del departamento de Santa Rosa, a conveniencia y tres institutos públicos de cada municipio. Con una muestra aleatoria simple de 587 adolescente, distribuida proporcionalmente. Se utilizaron los cuestionarios autoaplicables Children ?s Depression Inventory (CDI) y Screen for Child Anxiety Related Disorders (Scared) para detectar sintomatología depresiva y de ansiedad. Se obtuvo una participación de 56.4% de sexo femenino y 43.6% del sexo masculino. La prevalencia de sintomatos sugestivos de depresión fue de 23.7% y de ansiedad 61.2%, presentándose, ambas, con mayor frecuencia en el sexo femenino.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient 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 score0.146
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0010.000
Open science0.0040.001
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 head0.040
GPT teacher head0.397
Teacher spread0.358 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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