Time, location and frequency of snack consumption in different age groups of Canadians
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The location and time of snack consumption may influence the composition, nutrient content and portion sizes of snacks. In this study, we aimed to determine and compare the time, location and frequency of snack consumption among different age groups of Canadians. METHODS: Nationally representative dietary data from the 2015 Canadian Community Health Survey (CCHS) were used (19,677 participants aged ≥2 years). Dietary data were obtained using 24-h dietary recalls. Participants were categorized according to the frequency of snack consumption (1 time, 2-3 times, ≥4 times). The snack consumption over 24 h was divided into four time periods: before 10 am, 10 am to 3 pm, 3 pm to 8 pm and 8 pm to 12 am. Meal and snack location was categorized as at home; someone else's home; restaurants; and other. RESULTS: Snacking 2-3 times per day was the most common reported frequency (53.0%). Snacking at home (73.0%) was more prevalent than snacking away from home (27.0%). The most frequently reported time for snacking among Canadians was 3 pm to 8 pm (36.3%), and the least frequently reported time was before 10 am (8.1%). Snacking contributed to a high proportion of Milk and Alternatives (23.7%) and Vegetables and Fruit (23.4%) food groups and lesser proportions of Grain Products (15.4%) and Meat & Alternatives (9.4%) food groups among Canadians. CONCLUSIONS: Home is the main location, 3 pm to 8 pm is the main time, and 2-3 times per day is the most common reported frequency for snacking. A full understanding of snacking behaviors is needed in order to develop targeted strategies to improve the quality of snack food choices.
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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 | 0.000 |
| 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 it