Substance Abuse Monitoring by the Correctional Service of Canada
Bibliographic record
Abstract
The Correctional Service of Canada implemented a urine drug-testing program over a decade ago. Offenders residing in federal correctional institutions and living in the community on conditional release were subject to urine drug testing. The objective of this study is to describe this testing program and the extent of drug use by conditional release offenders in 2000. Urine specimens were tested for drugs of abuse and prescription drugs including amphetamines, cannabinoids, cocaine metabolite, opiates, phencyclidine, benzodiazepines, methyl phenidate, meperidine, pentazocine and fluoxetine by immunoassay screening followed by GC-MS confirmation. Ethyl alcohol was analyzed when specifically requested. Alternative screening and confirmation methods with lower cut-off values were used whenever urine specimens were dilute (creatinine <20 mg/dL and specific gravity ≤1.003). Total number of urine specimens analyzed in 2000 was 38,431 (6.7% were dilute). The positive rate for one or more drugs was 27.2% in 2000 in conditional release offenders. In the community setting 28,076 normally concentrated (nondilute) specimens were tested (9.6% were positive for cannabinoids and 3.3% positive for cocaine metabolite). In the 1,270 dilute specimens collected from conditional release offenders in 2000, 12.8% were positive for cannabinoids and 10.6% were positive for cocaine metabolite. The authors conclude that forensic urine drug testing provides an objective measure of drug use when assessing offenders living in the community on conditional release from correctional institutions in Canada.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".