Impact of Dietary Patterns and Nutritional Status on the Academic Performance of Omani School Students
Bibliographic record
Abstract
In a cross-sectional study, we determined the dietary patterns and nutritional status of Omani school students (12-15 years), and their association with student’s academic performance. A study questionnaire, including a semi-quantitative food frequency questionnaire, was used to collect data. Results indicated that 36% of Omani school students regularly consumed breakfast, whereas only 21.7% had daily 3 meals. Similarly, 30.5% of students consumed fruits ≥ 2 serving/day, 26.6% consumed vegetables ≥ 3 serving/day, and 49.8% consumed fish ≥ 2 serving/week. Significant differences were observed regarding breakfast consumption among genders, regular intake of daily 3 meals, fruits, fish, avoiding soft drinks, nutritional knowledge, total energy and macronutrient intake. Based on BMI, 12.3 % of students were overweight, and 26.1% were obese. The students did not have enough nutritional knowledge and showed unhealthy dietary patterns indicated by their mediocre Omani Diet Scores. Daily energy and macronutrient intakes in males were significantly higher than females. Only fish intake, avoiding soft drinks, waist to height ratio (WHtR), and nutritional knowledge score showed significant associations with student’s academic performance. Healthy dietary patterns and improved nutritional status of school students showed a positive association with their academic performance, suggesting that more focus should be placed in developing healthy dietary patterns.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".