Intake of Fruits, Vegetables, and Sugar-Sweetened Beverages among a Sample of Children in Rural Northern Ontario, Canada
Bibliographic record
Abstract
There is evidence to suggest that dietary intake of children differs by rural/urban place of residence: rural children may have a higher intake of foods high in fat and sugar than those living in urban environments. The aim of this study was to examine the intake of fruits and vegetables (FV) and the frequency of sugar-sweetened beverage (SSB) consumption, among a sample of rural children in Northern Ontario, Canada, in two different seasons. Sociodemographic factors and children's FV and SSB intake were measured using two repeated cross-sectional surveys, and seasonal information was based on the month of data collection. Logistic regressions were used to examine the odds of children eating five or more FVs, and the odds of 'frequently or always' consuming SSBs. During the fall, children reported eating five or more FV more often, when compared to winter (53.9% vs. 48.3%). In the fall, 25.8% of children reported 'frequently or always' drinking SSB, compared with 16.9% in winter. Indigenous children were less likely to eat five or more FV (OR 0.34 (95% CI 0.12-0.95)) in the fall when compared to non-Indigenous children. Findings indicate that intake of FV among rural students in this region is low, and the frequency of SSB is high, when compared with national recommendations.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".