Vegetarianism and mental health: Evidence from the 1970 British Cohort Study
Bibliographic record
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 cohort in Great Britain. METHODS: We use data from the 1970 British Cohort Study, which collected information on vegetarianism at age 30 in 2000 (n = 11,204) and psychological distress (PD) at ages 26, 30, 34, 42, and 46-48 in 2016/18. We first developed a statistical adjustment strategy by regressing PD at age 30 on vegetarianism and 14 confounders measured at ages 10 and 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. 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 using red meat consumption 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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".