Propiedades psicométricas de la Escala de Gaudibilidad (Moduladores de Disfrute) para Niños y Adolescentes (EGNA).
Bibliographic record
Abstract
La disminución de la capacidad de disfrute puede considerarse un factor de riesgo o indicador de presencia de trastornos mentales. Por ello, es de utilidad disponer de instrumentos válidos para evaluar la gaudibilidad en niños y adolescentes para desarrollar intervenciones que mejoren la gaudibilidad. El objetivo del presente trabajo fue construir y analizar las propiedades psicométricas de la Escala de Gaudibilidad para Niños y Adolescentes (EGNA). 1.264 estudiantes de primaria, secundaria y bachillerato respondieron a: EGNA, Escala de Autoestima de Rosenberg, Escalas Panas de afecto positivo y negativo para niños y adolescentes y la escala de Depresión del Centro de Estudios Epidemiológicos. Mediante un análisis factorial confirmatorio utilizando el método de estimación de máxima verosimilitud robusta se demostró un ajuste adecuado al modelo de 5 factores (Disfrute en compañía, Autoeficacia frente al aburrimiento, Sentido del humor, Imaginación e interés) relacionados. Además, se observó una consistencia interna adecuada (Cronbach α=0,794) en la escala total y en cada uno de los 5 factores. Finalmente se observó una adecuada validez concurrente y discriminante. La EGNA obtuvo adecuados indicadores respecto a su consistencia interna y validez. La EGNA es adecuada para cuantificar los niveles de gaudibilidad en niños y adolescentes. The decrease in the ability to enjoy can be considered a risk factor or marker of mental disorders. Therefore, it can be useful to have a scale to quantify gaudiebility in children and adolescents to develop strategies to improve gaudiebility. The objective of the present work was to build and analyze the psychometric properties of the Gaudiebility Scale for Children and Adolescents (GSCA). 1,264 primary, secondary and high school students responded to GSCA, Rosenberg’s self-esteem scale, Positive and Negative Affect schedule, Center of Epidemiological Studies of Depression scale. Through a confirmatory factor analysis using a robust maximum-likelihood estimation, an adequate adjustment was observed to 5 factors model (Enjoyment in Company, Self-efficacy versus boredom, Sense of humor, Imagination and Interest) related. In addition, an appropriate internal reliability (Cronbach α=0,794) was observed in the total scale and in the 5 subscales. Finally, an adequate concurrent and discriminant validities were observed. GSCA’s psychometric properties indicate appropriate internal reliability and validity. GSCA seems appropriate to quantify gaudiebility levels in children and adolescents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".