Examining the effect of social bonds on the relationship between ADHD and past arrest in a representative sample of adults
Bibliographic record
Abstract
BACKGROUND: Several studies have found a connection between attentional deficit hyperactivity disorder (ADHD) and criminal behaviour in clinical and prison samples of adults, but there is a lack of representative general population data on this. AIM: To test relationships between histories of ADHD and arrest. Our main research question was whether any such relationship is direct or best explained by co-occurring variables, especially indicators of social bonds. METHOD: Data were from a sample of 5,376 adults (18+) representative of the general population of Ontario, Canada. Logistic regression analysis was used to explore the relationship between self-reported arrest on criminal charges and ADHD as measured by the Adult Self Report Scale (ASRS-v1.1). Indicators of strong social bonds (post secondary education, household size) and weak bonds (drug use, antisocial behaviours, alcohol dependence) were also obtained at interview and included in the statistical models. RESULTS: In a main effects model, screening positive for ADHD was twice as likely (OR 2.05 CI 1.30, 3.14) and past use of medications for ADHD three times as likely (OR 3.94 CI 2.46, 6.22) to be associated with ever having been arrested. These associations were no longer significant after controls for weak and strong social bonds were added to the models. In the best fitting statistical model, ever having been arrested was not associated with ADHD, but it was significantly associated with indicators of strong and weak social bonds. CONCLUSIONS: The observed connection between ADHD and criminality may be better understood through their shared relationships with indicators of poor social bonds. These include antisocial behaviour more generally, but also drug use and failure to progress to any form of tertiary education, including vocational training. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".