Patterns of Red and Processed Meat Consumption across North America: A Nationally Representative Cross-Sectional Comparison of Dietary Recalls from Canada, Mexico, and the United States
Bibliographic record
Abstract
Close economic ties encourage production and trade of meat between Canada, Mexico, and the US. Understanding the patterns of red and processed meat consumption in North America may inform policies designed to reduce meat consumption and bolster environmental and public health efforts across the continent. We used nationally-representative cross-sectional survey data to analyze consumption of unprocessed red meat; processed meat; and total red and processed meat. Generalized linear models were used to separately estimate probability of consumption and adjusted mean intake. Prevalence of total meat consumers was higher in the US (73.6, 95% CI: 72.3-74.8%) than in Canada (65.6, 63.9-67.2%) or Mexico (62.7, 58.1-67.2%). Men were more likely to consume unprocessed red, processed, and total meat, and had larger estimated intakes. In Mexico, high wealth individuals were more likely to consume all three categories of meat. In the US and Canada, those with high education were less likely to consume total and processed meat. Estimated mean intake of unprocessed red, processed, and total meat did not differ across sociodemographic strata. Overall consumption of red and processed meat remains high in North America. Policies to reduce meat consumption are appropriate for all three countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".