Fast food consumption in adults living in Canada: alternative measurement methods, consumption choices, and correlates
Bibliographic record
Abstract
Global industries and technological advancements have contributed to the proliferation of fast food (FF) establishments and ultraprocessed food, associated with poorer diet quality and health outcomes. To investigate FF as an indicator, we compared alternative methods to capture self-reported FF consumption and examined associated socio-demographic factors. We conducted a secondary analysis of the 2014-2015 Foodbook study, a cross-sectional survey on foods consumed by Canadians during the previous week. An embedded randomized design compared alternative FF intake questions of varying details. A total of 6062 participants aged 18+ were included, representing 24.7 million Canadian adults. Approximately 48% consumed FF in the past week, and of FF consumers, average frequency was twice. Asking broadly about FF intake without examples resulted in significantly lower reported FF intake compared with the two more detailed questions; the latter two were not significantly different. Burgers, pizza, and submarines/sandwiches were most commonly consumed. Men, younger age, higher BMI, women in central Canada (versus territorial regions), and men with income $30 000-$80 000 (versus >$80 000) were associated with higher FF consumption. Consumption of FF is common among Canadians; some associated factors are gender-specific. Further research examining FF as an indicator, and individual and societal implications of FF consumption, is recommended to inform programs and policies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".