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Record W7030210668

Meat-reduced Dietary Practices and Efforts in Five Countries: Analysis of Cross-sectional Surveys in 2018 and 2019

2022· article· en· W7030210668 on OpenAlexaboutno aff

Bibliographic record

VenueOwn your potential (DEAKIN) · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupLogistic regressionRed meatSocioeconomic statusFood group
DOInot available

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.014
GPT teacher head0.280
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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