Macroeconomic impacts of reducing nutrition-related chronic disease by adopting a “healthier diet.”
Bibliographic record
Abstract
The demand for economic information by health policy makers is increasing in order to address policy questions concerning such things as research funding, intervention and policy selection. As household income increases, the consumption of saturated fats and sugars increase, while the consumption of cereals, fruits and vegetables decrease. This change in food consumption has been identified as a risk factor in the prevalence of nutrition-related chronic diseases such as: cardiovascular disease (CVD), diabetes and some cancers. Promotion and adoption of “healthier diets” in Canada would decrease the prevalence of nutrition-related chronic disease. This would have an impact on the well-being of individuals and households, would decrease the financial burden on society for these types of health related problems, and increase the productivity of the economy through increased labour efficiency. These changes in the demand for food and other commodities and increased productivity will have a macroeconomic impact on all sectors of the economy. The adoption of a “healthier diet” also has an impact on the production and trade of agriculture and agri-food commodities. As individuals and households diets change, the demand for agriculture and food products also changes, which has a direct and indirect impact on the Canadian economy. Changes to a “healthier diet” have an impact on agriculture production, food processing, trade and policies that affect these sectors. A GTAP model with eight regions was used to estimate the macroeconomic impacts of a change to a “healthier diet.” The impacts include changes in industrial output, GDP, employment and changes in trade patterns between Canada, the US, Mexico, Brazil and Chile.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".