Taking Stock of Fruit and Vegetable Consumption in Canada: Trends and Challenges
Bibliographic record
Abstract
Purpose: A diet rich in fruits and vegetables is vital for prolonged health and wellness. Yet, the consumption of fruits and vegetables remains low in some regions. Methodology: This exploratory quantitative study utilized a web-based survey instrument to probe the likelihood of consumption by Canadian consumers. Canadians who have lived in the country for 12 months or more and were 18 years of age or older were surveyed. Care was given to get a representative sample from all Canadian regions. Findings: Barriers to produce consumption include cost (39.5%), lack of knowledge and preparation skills (38.5%), and confusion surrounding health benefits (6.3%). There is further confusion surrounding the nutrition of frozen vs. fresh vegetables. Finally, respondents were concerned about pesticide residue on imported produce (63.4%). Originality: Although evidence that fruits and vegetables can mitigate disease and that promotion of fruit and vegetable consumption has been a key policy area for the Canadian government, consumers still fail to integrate sufficient fruits and vegetables into their diets. To our knowledge, this is the only study probing consumers on their fresh produce intake in the Canadian context. Public awareness and education about the regular consumption of fruits and vegetables and their nutritional value and health-promoting benefits can increase consumption in many Canadian regions and demographics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".