Alcohol and drug problems: a multivariate behavioural genetic analysis of co-morbidity
Bibliographic record
Abstract
Multivariate biometrical genetic analyses of self-report questionnaire items assessing problem alcohol and drug use were performed on data obtained from a sample of 438 volunteer twin pairs (236 monozygotic twin pairs, 247 dizygotic twin pairs). Additive genetic influences were moderate for all alcohol abuse items (21-46%), frequency of drug use (32%) and illicit drug use (32%). Prescribed drug use and debilitating drug use were largely environmentally determined (86% and 94%, respectively). The influence of environmental factors that influence all members of a family to the same degree (shared family environment) on each item was generally small (0-20%), whereas the influence of environmental factors unique to each family member (non-shared environment) comprised over half of the total variance on all items. Genetic factor analyses identified three uncorrelated common genetic factors. The first genetic factor appears to represent problems associated with alcohol and drug use, such as the inability to fulfil obligations at home, work or school. The second genetic factor is more specific to drug use and represents a general liability towards drug use, illicit or otherwise. The third genetic factor is specific to the alcohol use items only. The observed co-morbidity of alcohol and drug misuse can be attributed largely to a non-shared environmental factor common to both domains. Genetic co-morbidity appears to be limited to alcohol and substance misuse behaviours that interfere with normal daily functioning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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 | 0.000 | 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".