Vegetarianism and mental health: longitudinal evidence in the 1970 British Cohort Study
Bibliographic record
Abstract
ABSTRACT Background Reducing animal product consumption has benefits for population health and the environment. The relationship between vegetarianism and mental health, however, remains poorly understood. This study explores this relationship in a nationally representative birth cohort in Great Britain. Methods We use data from the 1970 British Cohort Study, which collected information on diet at age 30 ( n = 11,204) and psychological distress (PD) using the nine-item Malaise Inventory at ages 26, 30, 34, 42, and 46-48. We first developed a statistical adjustment strategy by regressing PD at age 30 on vegetarianism and 14 potential confounders measured at ages 10 and 26 (including PD at age 26). We then ran multilevel growth curve models, testing whether within-person changes in PD between ages 30 and 46-48 differed by vegetarianism, before and after statistical adjustment. Models were reproduced using red meat consumption at age 30 as a sensitivity analysis. Results At age 30, 4.5% of participants reported being vegetarian. In the cross-sectional models at age 30, vegetarians reported more distress compared with non-vegetarians in bivariate analysis (b = 0.30, 95%CI 0.09, 0.52), but this difference disappeared in the fully-adjusted model (b = 0.02, 95%CI -0.17, 0.21). In the longitudinal models between ages 30 and 46/48, there were no differences in within-person changes in psychological distress between vegetarians and non-vegetarians ( p = .723). Sensitivity analyses yielded similar findings. Conclusion In this British cohort, vegetarianism at age 30 was not associated with changes in psychological distress during mid-adulthood. Since psychological distress in early adulthood predicted vegetarianism at age 30, more studies are needed to disentangle the progression of this relationship over the life-course.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".