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Record W4214884168 · doi:10.21134/haaj.v22i1.652

“USANDO CONDÓNâ€: UNA INTERVENCIÓN CONDUCTUAL CUASI-EXPERIMENTAL BASADA EN TEORÃA PARA MEJORAR LA AUTOEFICACIA PERCIBIDA DEL USO DEL CONDÓN EN ADOLESCENTES MEXICANOS

2022· article· es· W4214884168 on OpenAlexaff
Alma Angélica Villa-Rueda, Erick Landeros-Olvera, Mostafa Shokoohi

Bibliographic record

VenueAfrican journal of rhetoric · 2022
Typearticle
Languagees
FieldHealth Professions
TopicHealth and Lifestyle Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHumanitiesPsychologyPhilosophy

Abstract

fetched live from OpenAlex

Objetivo: Las infecciones de transmisión sexual y el embarazo representan problemas de salud pública en la ado­lescencia, prevenibles con el uso consistente del condón. Este artículo presenta la eficacia preliminar de “Usando Condón”, una intervención teórica-conductual para aumentar la autoeficacia percibida para el uso del condón. Mé­todo: “Usando Condón” consta de dos sesiones de 90-minutos dirigida a adolescentes mexicanos de 15 a 19 años. El muestreo se dividió en tres fases: a) aleatorización de preparatorias; b) aleatorización de preparatorias seleccio­nadas; c) muestreo no probabilístico de participantes. Se incluyeron 392 adolescentes distribuidos en tres grupos: grupo experimental (n = 132), grupo control-folleto (n = 130) y grupo de control-control (n = 130). La intervención se evaluó con la Escala de Auto-eficacia para el Uso del Condón en Adolescentes Mexicanos. Se utilizó estadística inferencial. Resultados: Los grupos fueron equivalentes (variables sociodemográficas), a excepción de la edad (p > .05). Se encontró diferencia estadística significativa entre los controles y el grupo experimental en la post prueba (p <.05, F = 18.089, IC 95%). Conclusión: “Usando Condon” aumentó los niveles de autoeficacia percibida para el uso del condón. Se necesita seguir probando la intervención en contextos diferentes.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.391
Teacher spread0.345 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations1
Published2022
Admission routes1
Has abstractyes

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