Disparities in the frequency of fruit and vegetable consumption by socio-demographic and lifestyle characteristics in Canada
Bibliographic record
Abstract
BACKGROUND: The health benefits of adequate fruit and vegetable (F&V) consumption are significant and widely documented. However, many individuals self-report low F&V consumption frequency per day. This paper examines the disparities in the frequency of F&V consumption by socio-demographic and lifestyle characteristics. METHOD: This study uses a representative sample of 93,719 individuals from the Canadian Community Health Survey (2007). A quantile regression model is estimated in order to capture the differential effects of F&V determinants across the conditional distribution of F&V consumption. RESULTS: The conditional and unconditional analyses reveal the existence of a socioeconomic gradient in F&V consumption frequency, in which the low income-education groups consume F&V less frequently than the high income-education groups. We also find significant disparities in F&V consumption frequency by demographic and lifestyle characteristics. The frequency of F&V consumption is relatively lower among: males, those in middle age, singles, smokers, individuals with weak social interaction and households with no children. The quantile regression results show that the association between F&V consumption frequency, and socio-demographic and lifestyle factors varies significantly along the conditional F&V consumption distribution. In particular, individual educational attainment is positively and significantly associated with F&V consumption frequency across different parts of the F&V distribution, while the income level matters only over the lower half of the distribution. F&V consumption follows a U-shaped pattern across the age categories. Those aged 30-39, 40-49 and 50-59 years consume F&V less frequently than those aged 18-29 years. The smallest F&V consumption is among the middle aged adults (40-49). CONCLUSIONS: Understanding the socio-demographic and lifestyle characteristics of individuals with low F&V consumption frequency could increase the effectiveness of policies aimed at promoting F&V consumption. The differential effects of individual characteristics along the F&V consumption distribution suggest the need for a multifaceted approach to address the variation in F&V consumption frequency.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".