Testing a novel multicomponent intervention to reduce meat consumption in young men
Bibliographic record
Abstract
Both epidemiological studies and randomised controlled trials have shown that meat-eating can be harmful to human health. Meat-eating is also considered to be a moral issue, impacting negatively on the environment and the welfare of animals. To date, very little scientific research has aimed to reduce this dietary behavior. Therefore, the current research tests the effectiveness of a 4-week multicomponent intervention designed to reduce meat-eating. Using a randomised controlled trial procedure, thirty-two young men (mean age: 23.5 ± 3.1 years old) were randomly assigned into two equal groups, the intervention vs control group. Based on research in social and health psychology, the intervention was composed of five components expected to reduce meat consumption: a social norm component; an informational/educational component; an appeal to fear; a mind attribution induction; and a goal setting/self-monitoring component. Measures of different types of meat intake (using dietary journals) were taken at baseline (Time 1) as well as 2 (Time 2) and 4 weeks later (Time 3). Emotions and attitudes toward meat-eating and animals were also assessed at Time 3. Significant reductions in total and weekend red meat consumption as well as cold cuts consumed on the weekend were observed in the intervention condition from Time 1 to Time 3. Moreover, reduced positive emotions toward eating meat mediated the reduction in red meat consumption. The component of the intervention that participants most often perceived as having led to a reduction in their meat consumption was the informational component. In conclusion, results provide support for the effectiveness of the multicomponent intervention and for the mediating role of positive emotions when predicting behavioral changes in meat consumption.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".