Eficacia de una intervención educativa escolar para la prevención de la obesidad infantil
Bibliographic record
Abstract
Introduction. The prevalence of childhood obesity has increased considerably since 2000, acquiring the dimension of an epidemic, with 5.9% of children under 5 years of age being overweight. Modifiable causative factors include lifestyle, sleep, diet and physical activity. The objective is to evaluate the effectiveness of an educational intervention in a school environment to modify the habits of children aged 6-12 years in 3 areas, diet, physical activity and screen time. \n \nMethodology. The sample is 540 children, in primary education stage distributed, in three groups: a) diet and screen time intervention, b) physical activity and screen time and c) diet, physical activity and screen time. The educational intervention includes contents of the plate method, food pyramid and exercise based on the Canadian physical activity guide for children aged 5 to 11 years. Age variables and anthropometric measurements are collected. In instrumentation, the KIDMED Test (diet adherence), “IPAQ-A” Questionnaire (physical activity), and screen time questionnaire (use of electronic devices) are considered. Data collection is carried out pre-intervention, post-intervention and one month after the end of the intervention. The analysis considers descriptive statistics for the distribution and values of the quantitative and qualitative variables and analytical statistics for the analysis of the effectiveness of the intervention as appropriate to the type of variables such as Student's t test, ANOVA, Chi square, Pearson correlation. The data will be analyzed with the SPSS statistical system.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".