Self-Reports of Illegal Activity, SCL-90–R Personality Scales, and Urine Tests in Methadone Patients
Bibliographic record
Abstract
In routine work, medical staff usually has to rely on the patient's self-reports of criminal activity and of recent involvement in fights. This study examines how these self-reports of crime correlate with the patients' routine urine tests and personality measures. Pearson correlations of these self-reports by 55 methadone patients (M age = 34.1 yr., SD = 9.1; 35 men, 20 women) were calculated to their urine screening tests (those for opiates, benzodiazepines, and cocaine) and to personality scores on the Symptom Checklist 90-Revised (SCL-90-R). Patients who reported being involved in recent illegal activities to obtain drugs had significantly higher scores on the SCL-90-R scale assessing obsessive-compulsive symptoms (r = .28) and had more frequent positive urine tests for cocaine (r = .35). Those who reported having engaged in fights within the last 12 mo. had higher scores on SCL-90-R measures of somatic complaints (r = .32), anxiety (r = .31), and depression (r = .29), and of overall psychopathology (r = .29), and they also had more often positive urine tests for cocaine (r = .28) than other patients. Studies on larger samples are needed to help clinicians to predict criminal or hostile behavior during methadone treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".