Evaluation of Fruit Juice Intake and Body Mass Index Within a Sample of Ontario Preschoolers
Bibliographic record
Abstract
This study describes fruit juice consumption and associated factors for a sample of Ontario preschoolers and determines potential relationships between intake and body mass index (BMI). Secondary data analysis was conducted on growth, dietary, and demographic data collected during the validation of NutriSTEP (Nutrition Screening Tool for Every Preschooler) with 254 preschoolers, aged 3 to 5 years. Dietary data were collected through parent-completed 3-day food intake records. BMI was determined using child weight and height measurements taken by a registered dietitian. Demographic characteristics were gathered using an 8-item standardized questionnaire adapted from Statistics Canada. Bivariate analyses were performed to determine associations with juice intake, BMI category, and child, parental, and family characteristics. Almost one quarter (23.6%) of the preschoolers were at risk of being overweight or were overweight, 73.2% were within the normal range, and 3.1% were underweight. Overall, 88.1% of the children consumed 100% fruit juice during the intake period, with a mean intake of 210 ± 183.1 mL/d. Fifty-seven percent consumed more than 125 mL/d of fruit juice, and 31.1% consumed more than 250 mL/d, whereas US recommendations limit intake to 125 to 175 mL/d. No significant differences were seen in fruit juice intake among the BMI categories or with selected child, parental, or family characteristics. Consuming 100% fruit juice does not appear to be associated with preschoolers' BMI, but further longitudinal research is needed in larger, more diverse groups to confirm this finding. Meanwhile, increased parent education around appropriate beverage intake, including fruit juice, is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".