PSIV-4 Program Chair Poster Pick: Determinants of red meat exclusion from diets in Canada
Bibliographic record
Abstract
Abstract Production of red meat including beef, pork, and lamb, has been associated with climate change and high intakes of these foods have been linked to risks of several leading chronic diseases. Reducing red meat consumption has been suggested as an option to address important health and sustainability challenges. Characterizing the sociodemographic factors associated with red meat consumption is an important first step in identifying strategies to translate information regarding sustainable food choices into policy and national dietary guides. The objective of this study was to characterize the demographic factors associated with the exclusion of red meat in consumer diets. Mixed-effects probit regression that accounts for the hierarchical structure of individuals clustered in ten provinces in 24-hr dietary recall data from the 2015 Canadian Community Health Survey (n = 10,117) was used to identify factors associated with dietary choices. Despite growing public discourse regarding the elimination of red meat, the results indicate that fewer than 5% of Canadians reported excluding red meat from their diet. Sex, education level, and race/ethnicity had a significant effect on red meat exclusion with single females (P < 0.000), individuals with at least a Bachelor’s degree (P < 0.001), and individuals who self-identified as African (P < 0.001), Asian (P < 0.001), and Oceanian (pP < 0.001) origin more likely to eliminate red meat. In contrast, households with children under age 25 (P < 0.001) were less likely to do so. The disparities in consumption patterns of red meat by sex, race/ethnicity, education, and family status can inform public education and policy initiatives using science-based information to improve the health and environmental sustainability associated with the Canadian diets.
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.002 |
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".