Home packed food for nursery children in United Arab Emirates provides suboptimal quality
Bibliographic record
Abstract
BACKGROUND: Early childhood nutrition is associated with health outcomes later in life, hence developing health promoting habits from an early age is imperative. OBJECTIVE: The aim of this study was to assess the nutritional adequacy of home-packed food brought to the nurseries by attending children. METHODS: In a cross sectional study conducted in 7 nurseries in Abu Dhabi, United Arab Emirates 315 food-boxes were assessed through detailed food observations at the nurseries prior to mealtimes. The food content was evaluated using the Alberta Guidelines for nursery food, Canada. RESULTS: Most food boxes contained refined grains (77.5%), fruits (74.6%), sweet/full fat dairy products (77.5%), discretionary-calorie-food-items (70.6%). Emirati children were offered sweetened drinks significantly more ( p < 0.001). Non-dairy protein sources, vegetables, low-fat-natural-dairy products were offered to 45.4%, 44.1% and 3.9% of children, respectively. Overall, 70.2% of the food-boxes contained not-recommended food and 63.1% of the children were served a very poor food combination. CONCLUSIONS: Despite frequent inclusion of recommended food, many food boxes were nutritionally inadequate due to their low content of whole grains, low-fat dairy products, vegetables and animal proteins and high content of sweet food and drinks. The inadequate dietary patterns necessitate developing nutrition guidelines for nurseries in Abu Dhabi.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".