Differential Effectiveness of Lifestyle Modification and Nutrition Education in Enhancing Nutritional Knowledge and Healthy Eating Practices Among Pupils, Nigeria
Bibliographic record
Abstract
The eating patterns of children in the last few decades along with the general population have changed. The population now consume a greater amounts of high-fat, high-salt, and high-sugar foods which have constituted numerous health challenges. This study examined the differential effectiveness of nutrition education and life modification intervention on healthy eating practices among primary school pupils in Ikenne Local Government Area of Ogun State, Nigeria. A sample of 240 primary 5 and 6 pupils from 3 primary schools were randomly selected from three public primary schools in Ikenne LGA of Ogun State, Nigeria. One standardized instrument was used in collecting data while analysis of covariance statistical method was used to analyze the generated data. Results showed a significant differential effectiveness of lifestyle modification (LM), nutrition education (NE) and when combined as a treatment (LM/NE) enhanced participants’ nutritional knowledge and healthy eating behaviour. The study also revealed that female pupils in all the three groups benefited more compared to their male counterparts after treatment. This study concluded that the treatment programmes are veritable tools in enhancing the knowledge of personal food intake and this knowledge will increase the individuals' potential and decisions to continue to utilize nutrition information in the choice of daily food and monitoring salt and sugar intake to prevent diet-related illness. Therefore, enhancing healthy living among the citizens.
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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 | 0.001 |
| 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) | 0.001 | 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".