Assessing the Impact of Fruits and Vegetable Consumption and Food Insecurity on Chronic Diseases in Canada
Bibliographic record
Abstract
Chronic diseases such as cardiovascular disease, diabetes, cancer, and high blood pressure are leading causes of morbidity and mortality in Canada, accounting for 89% of deaths annually and imposing an economic burden of $190 billion each year. These conditions are closely linked to dietary patterns, yet fruit and vegetable consumption remains below the recommended 400 grams per day for many Canadians. Simultaneously, food insecurity affects 17.8% of Canadian households, representing approximately 8.7 million people, including 2.1 million children, with rates as high as 46% in territories like Nunavut, exacerbating health disparities.This thesis analyzes the impact of fruit and vegetable consumption and food insecurity on chronic disease prevalence using Canadian Community Health Survey data (2004–2020). The study employs logistic regression models to assess these relationships at national and provincial levels, as well as across demographic and socioeconomic groups. Higher fruit and vegetable consumption at the national level was significantly linked to reduced chronic disease risks, including a 13.3% lower likelihood of developing high blood pressure across Canada. However, these effects varied across provinces, reflecting differences in dietary patterns. In Quebec, high fruit and vegetable intake reduced diabetes odds by 14%, whereas in Newfoundland, starchy vegetable consumption, such as potatoes and carrots, was associated with a 32.5% increase in cardiovascular disease risk, highlighting the regional impact of specific dietary habits on health outcomes. Food insecurity was significantly associated with poorer health outcomes, including higher prevalence rates of diabetes and hypertension. Food-insecure households were more likely to rely on nutrient-poor, calorie-dense foods, exacerbating chronic disease risks, particularly among low-income populations.The findings underscore the need for targeted public health interventions to improve dietary habits and food access. Effective strategies, such as subsidies, school-based programs, community initiatives, and federally supported programs like Nutrition North Canada, can enhance fruit and vegetable intake, particularly in remote and underserved regions. Addressing these challenges is crucial to reducing chronic disease prevalence and improving population health outcomes across Canada
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".