National School Nursing Policy to Promote and Improve the Health of Overweight and Obese Children in Saudi Arabia
Bibliographic record
Abstract
Introduction: The health and well-being of school students are critical to communities globally, and the incidence of overweight and obesity in children and adolescents has become a global epidemic. In Saudi Arabia, childhood obesity is a significant concern, but no community interventions have been developed. Policymakers and health and social service providers should develop and implement policies that promote healthy living in individuals, including both physical activity and healthy nutrition programs. Objective: We established a school nursing policy to promote and improve the health of overweight and obese children in Saudi Arabia. Methods: This policy adheres to the guidelines of the University of Toronto's Health Promotion Center. Conclusion: It is critical to establish a national school nursing policy to promote healthier lifestyles among school-aged children in Saudi Arabia, since childhood overweight and obesity are significant issues, not just for achieving the goals of Vision 2030 but also for improving individual lives. However, this requires significant cooperation between the MoH and the MoE. In turn, such policies will benefit both ministries and the community as a whole by reducing healthcare costs and reducing children’s intake of fast foods, sweets, fatty foods, and sugary soft drinks and increasing physical activity.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".