Attention deficit hyperactivity disorder as a neglected psychiatric disease in prison: Call for identification and treatment
Bibliographic record
Abstract
Mis-diagnosis of attention deficit hyperactivity disorder (ADHD) is an important public health concern because the disease is treatable, yet can have a disastrous effect on the life of those affected. ADHD is associated with delinquency, criminality, and recidivism; and thus, people living in detention are especially at risk of having ADHD. This study investigated prevalence rates of ADHD diagnosis and treatment in prison. Data were collected in a Swiss prison (n=158). Medical files were screened for ADHD clinical diagnosis and treatment, and participants completed five items assessing ADHD symptomatology (ASRS-5). We computed prevalence rates with 95% confidence intervals (CI). Overall, 1.9% [95% CI: 1.1%–5.8%] of the participants had a clinical diagnosis of ADHD in medical files. Nobody received ADHD treatment. For the self-reported questionnaire, 12.9% [95% CI: 8.5%–19.2%] of the participants met the cut-off and were screened as potentially having ADHD. This study suggested that ADHD was under-diagnosed and under-treated in prison, with a lower prevalence rate according to the medical files of the participants in comparison with self-reports and with the worldwide meta-analytic prevalence rate of 26.2%. ADHD should receive more attention in order to promote health equity between incarcerated and general populations, to reduce health (care) disparities, and to enhance rehabilitation following incarceration.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".