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Record W7061559722

Prevalencia de alexitimia en estudiantes de una universidad nacional

2017· dissertation· es· W7061559722 on OpenAlexaboutno aff

Bibliographic record

Venuerenati · 2017
Typedissertation
Languagees
FieldPhysics and Astronomy
TopicGyrotron and Vacuum Electronics Research
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Exploratory researchWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

La presente investigación tiene como objetivo principal determinar la prevalencia de alexitimia en estudiantes de una universidad nacional. Asimismo, se pretende comparar las diferencias para cada variable (Sexo, Grupo de Edad, Año de Estudio, Especialidad); y, finalmente, realizar un análisis psicométrico del instrumento a utilizar, la Escala de Alexitimia de Toronto (TAS 20). La muestra estuvo comprendida por 223 estudiantes de psicología, de ambos sexos, con un rango de edad entre los 17 y 28 años, de 1ro a 6to año de estudio y de las 4 especialidades (Clínica, Social, Educativa y Organizacional). Los resultados arrojaron que la Escala de Alexitimia de Toronto presenta un Alfa de Cronbach de .83, así como, validez en su estructura. De acuerdo a la prevalencia total, se encontró que el 26% de la muestra son considerados como casos de alexitimia.Respecto a la variable Sexo, son las mujeres quienes obtuvieron un porcentaje mayor de casos de alexitimia con el 60%. En cuanto al grupo de edad, porcentajes altosse hallaron en el segundo grupo (De 20 a 22 años) con un 51.7%; del mismo modo, de acuerdo a la variable año de estudio, los de 2do Año arrojaron un porcentaje de 25% superando a los otros años y, según la especialidad,son los deláreaClínicaquienes obtuvieronun porcentaje mayor de alexitimia con un 48%.A pesar de estos resultados, no se hallaron diferencias significativas entre las medias de las variables. Finalmente, se sugiere elaborar estrategias de intervención primaria; es decir, de promoción y prevención para contrarrestar estos datos.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.344
Teacher spread0.330 · 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 designTheoretical or conceptual
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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