School-Based Nutrition Education Intervention Improves Nutrition Knowledge and Lipid Profile among Overweight/Obese Children
Bibliographic record
Abstract
Many children in Ghana do not meet the dietary and physical activity recommendations for their health due to several reasons including limited nutrition education intervention (NEI) programmes. NEI provides children with information on knowledge, attitudes and practices (KAPs) required to ensure proper dietary intake and physical activity. In this intervention study, we recruited eighty (80) overweight and obese students aged 11-15 years from two schools in the Ga-East municipality of Ghana. Anthropometric, biochemical, dietary and physical activity information were collected on the two groups before and after three months of NEI. Between group comparisons (test and control); before and after interventions were performed using student t-tests. It was shown that NEI improved nutrition knowledge (mean change = 5.13, p<0.01), attitude (mean change = 2.75, p<0.01) but not practice (mean change = -1.42, p<0.05) in overweight and obese children. Although anthropometric indicators did not improve with NEI, serum lipid profile of participants improved as indicated by the following mean changes: TC [-1.22, 95%CI (-1.90 -0.55)] mg/dL, HDL-c [-0.19, 95% CI (-0.38, 0.00)] mg/dL, LDL-c [-0.90, 95%CI (-1.52, -0.28)] mg/dL and TG [(-0.66, 95%CI (-1.23, -0.09)] mg/dL. Our findings show that NEI undertaken within a relatively short period of time could have positive effects on lipid profile, knowledge and attitudes of school children, and in turn, promote the fight against childhood obesity, and improve the health and wellbeing of children.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".