Food knowledge is associated with fruit and vegetable intake among children aged 9–14 years in Ontario, Canada
Bibliographic record
Abstract
It is widely accepted that the consumption of fruits and vegetables (FV) is important for maintaining overall health. Food-based dietary guidelines and national recommendations such as Canada's Food Guide (2019) encourage the frequent intake of FV, but despite such recommendations, the intake of FV by school-age children in Canada remains suboptimal Attempts to improve dietary quality have had modest success The Canadian government has recently prioritised the development of strategies that improve healthy eating early in childhood To inform public health professionals, policy makers, and educators about the factors associated with inadequate intake of FV, the objectives of this study were to a) quantify the intake of FV as reported by elementary school children in Ontario, Canada, and b) investigate factors associated with FV consumption, including food knowledge, the food environment, socioeconomic status, and other sociodemographic characteristics. In 2017-19, a cross-sectional survey was administered to 2,443 students aged 9 to 14 years, at 60 urban and rural schools across Southwest Ontario, Canada. The self-report survey included 124 questions under four main topics: sociodemographic information, eating habits, nutrition and food knowledge, and food preferences. A parent survey was used to validate the sociodemographic variables reported by students. FV intake was obtained from two multiple component food frequency questions, developed by registered dietitians. Forty-six individual questions assessed food knowledge in the student survey; a total score was obtained by adding the correct responses. Multiple regression was used to analyse children's intake of FV with various predictor variables, including food knowledge, sociodemographic characteristics, and the food environment. The mean intake of fruits and vegetables reported by participants was 2.6 (SD 1.1) and 2.4 (SD 1.2) servings/day, respectively. A total FV intake below WHO guidelines (5 servings/day) was reported by 40.7% of respondents. Mean total knowledge score was 29.2 (SD 7.1) out of a possible 46 points (63.5% correct responses). Knowledge score ( = 0.257, p < 0.001) and child age (= -0.072 p = 0.001) significantly predicted higher reported intake of FV. This study shows that FV intake among this sample of school-aged children is low, and increased intake is associated with higher food knowledge. To encourage healthy eating, school-based food and nutrition programmes of sufficient duration, that incorporate multiple components and emphasise food knowledge have value among this young population.
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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 | 0.000 | 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 | 0.001 |
| 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".