Predictors of Plant-Based Alternatives to Meat Consumption in Midwest University Students
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence of plant-based alternatives to meat consumption in students at a Midwest university, describe associations between demographics, environmental concern attitudes, and consumption, and determine variables statistically associated with trying the plant-based alternatives. DESIGN: Descriptive cross-sectional convenience sample; self-administered online surveys. SETTING: College students at a Midwest university. PARTICIPANTS: Currently enrolled students aged 18-30 taking courses on campus as of March 2020. MAIN OUTCOME MEASURES: Plant-based alternative consumption; demographics; vegetarian status; environmental attitudes; influences on food choices; and trusted sources of food information. ANALYSIS: Bivariate comparisons for consumption of plant-based alternatives; logistic regression analysis. RESULTS: Fifty-five percent had tried a plant-based meat alternative. Top reasons were enjoying new foods and curiosity about the products. Out-of-state residency, vegetarian status, and 10 of 11 environmental attitude statements were significantly associated with plant-based alternative consumption (P < 0.05). About 30% of consumers indicated they wanted to eat less meat and that plant alternatives were better for the environment. Nonconsumers had less favorable views of meatless meals. CONCLUSIONS AND IMPLICATIONS: This study supports that positive environmental attitudes were predictive of plant-based alternative consumption among college students. Increased awareness and familiarity could encourage consumption among this population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".