Meat-reduced Dietary Practices and Efforts in Five Countries: Analysis of Cross-sectional Surveys in 2018 and 2019
Bibliographic record
Abstract
Background: Diets that reduce reliance on animal-source foods are recommended in some contexts.\n Objectives: This study aimed to compare proportions of respondents who reported following meat-reduced dietary\n practices (i.e., vegetarian, vegan, or pescatarian diets) and/or making efforts to reduce animal-source foods, and to\n examine sociodemographic correlates across 5 countries.\n Methods: Online surveys were conducted in November and December 2018 and 2019 with 41,607 adults from Australia\n (n = 7926), Canada (n = 8031), Mexico (n = 8110), the United Kingdom (n = 9129), and the United States (n = 8411)\n as part of the International Food Policy Study. Respondents were asked whether they would describe themselves as\n vegetarian, vegan, or pescatarian, and whether they had made efforts to consume less red meat, less of all meats, or less\n dairy in the past year. Logistic regressions examined differences in the likelihood of each behavior between countries\n and sociodemographic subgroups.\n Results: Approximately 1 in 10 respondents reported following a vegetarian, vegan, or pescatarian diet, ranging from\n 8.6% (Canada) to 11.7% (UK). In the past 12 months, the proportions of respondents who reported efforts to consume\n less red meat ranged from 34.5% (Australia) to 44.4% (Mexico), less of all meats ranged from 27.9% (US) to 35.2%\n (Mexico), and to consume less dairy ranged from 20.6% (UK) to 41.3% (Mexico). Respondents were more likely to report\n efforts to consume less animal-source products in 2019 compared to 2018 in most countries. Sociodemographic patterns\n varied by country; in general, women, those with higher education levels, and those in minority ethnic groups were more\n likely to report following meat-reduced dietary practices or efforts to consume fewer animal-source products.\n Conclusions: Nearly half of respondents reported following a meat-reduced diet or efforts to reduce animal-source\n products, with differences between countries and population subgroups. Population-level approaches and policies that\n support meat reduction may further reduce consumption of animal-source products.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".