A Meta-Data Analysis on the Primary and Secondary Health Care Needs to Prevent Obesity among the High School Students in the United States
Bibliographic record
Abstract
Obesity in schools has grown to become one of the most dramatic features of the global obesity epidemic, with long-term consequences. It has become a public health challenge globally. This study is a meta data analysis of recently published articles in the refereed journals. Fifteen articles were purposively selected based on the criteria of obesity among the high school students and were analyzed to find the recent changing trends of obesity in schools. The analysis of studies demonstrates that school-aged children in the United States have a high prevalence of obesity. The findings provide evidence for prevention intervention strategies to reduce obesity in school-age children. The finding shows that there is a need for more greater awareness of the issues of obesity in schools and the author recommends the ways and means to reduce the obesity in schools in the United States. It is recommended that a healthy lifestyle during high school age is a must for teenagers to avoid obesity.
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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 | 0.027 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.055 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".