Mental disorders and discrimination: a prospective cohort study of young twin pairs in Germany
Bibliographic record
Abstract
ABSTRACT Background Mental disorders and discrimination share common risk factors. The association between having a mental disorder and experiencing discrimination is well-known, but the extent to which familial factors, such as genetic and shared environmental factors, might confound this association, including gender differences in familial confounding, remains unexplored. Aims We investigated potential unmeasured familial confounding in the relationship between mental disorders and discrimination. Method We examined 2,044 same-sex twin pairs aged 16–25 years from the German population-based study ’TwinLife’ . We used a matched design and random-effects regression applied to within-individual and within-and-between pair models for the association between mental disorder and discrimination, and used likelihood ratio tests (LRTs) to compare these models. Multivariable models were adjusted for body-mass-index, educational attainment, and global life satisfaction. Results Mental disorder and discrimination were associated in the adjusted within-individual model (adjusted odds ratio=2.19, 95% Confidence Interval:1.42–3.39, P <0.001). However, the within-and-between pair model showed that this association was explained by the within-pair mean (aOR=4.24, 95%CI:2.17–8.29, P <0.001) and not the within-pair difference (aOR=1.26, 95%CI:0.70–2.28, P =0.4) of mental disorder. Therefore, this association was mostly explained by familial confounding, which is also supported by the LRTs for the unadjusted and adjusted models ( P <0.001 and P =0.03, respectively). This familial confounding was more prominent for males than females. Conclusions Our findings show that the association between mental disorder and discrimination is almost fully explained by unmeasured familial factors. Incorporating family members in interventions targeted at ameliorating mental ill-health and experiences of discrimination among adolescents may improve efficacy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".