Imagen Corporal y Obesidad mediante las Siluetas de Stunkard en Niños y Adolescentes Indios de 8 a 15 Años
Bibliographic record
Abstract
Este artículo se centra en estudiar la imagen corporal y la sobrecarga ponderal (sobrepeso y obesidad) en niños y adolescente de India. La muestra estuvo compuesta por 395 participantes de 8-15 años de Karbi Anglong, Assam (212 de sexo masculino y 183 de sexo femenino). El instrumento utilizado fue el de las siluetas de Stunkard. El 37.3% de hombres y el 22,4% de mujeres fueron clasificados en la categoría de sobrepeso/obesidad ( =13,033; p=,001; d=0,63). Respecto a la imagen corporal, el 82,3% presentó insatisfacción con su cuerpo, destacando aquellos a los que les gustaría ser más grandes (hombres 62,7% y mujeres 49,7%). Por tanto, el problema psicológico de la insatisfacción corporal tuvo una mayor prevalencia que el problema fisiológico de la obesidad. Se recomienda implementar programas de actividad física para mejorar la imagen corporal y la composición corporal de los niños y adolescentes de Assam. The present study focused on the study of body image and overweightand obesityin Indian children and adolescents. This investigation involved 395 participants (212 males and 183 females) from Karbi Anglong, Assam,aged between 8 and 15 years (12.16 ± 2.01). The instrument used was Stunkard’s et al. silhouettes. 37.3% men and 22.4% women were classified as overweight/obese (!"#=13.033; p=.001; d=0.63). Regarding body image, 82.3% were dissatisfied with their body, highlighting those who would like to be larger (men 62.7% and women 49.7%). Therefore, the problem of body dissatisfaction had a higher prevalence than the physiological problem of obesity. It is recommended to implement physical activity programs to improve body image and body composition of children and adolescents in Assam.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 source (direct Gemma or distilled Codex), 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".