Water and Beverage Consumption among a Nationally Representative Sample of Children and Adolescents in the United Arab Emirates
Bibliographic record
Abstract
There are limited studies examining water consumption among individuals in hot climates. We assessed the daily total water intake from plain water, other beverages, and food in a nationally representative sample of children and adolescents aged 6-18 years in the United Arab Emirates. Total water intake was compared against the recommendations of the Institute of Medicine and the European Food Safety Authority. Sociodemographic information, 24 h dietary recall, physical activity levels, and anthropometric data were collected from 527 participants. The mean ± SE of total water intake was 1778.4 ± 33.8 mL/day. Plain drinking water was the largest contributor to total water intake (51.6%), followed by food (27.3%). Sugar-sweetened beverages constituted 13.9% of water intake. The proportion of participants who met the Institute of Medicine recommendations ranged from 15% (males aged 14-18) to 25% (children aged 6-8). The proportion of participants who met the European Food Safety Authority recommendations ranged from 31% (females aged 14-18) to 36% (males aged 14-18). The water-to-energy ratio was 1.0-1.15 L/1000 kcal, meeting recommendations. The majority of participants failed to meet water intake recommendations, highlighting the need for targeted interventions to promote increased water consumption among children and adolescents.
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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 | 0.001 | 0.001 |
| 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".