Detection of contraband drugs in forensic-correctional mental health services using TeknoScan-a gas chromatography tool
Bibliographic record
Abstract
Substance misuse is a major problem among individuals involved in forensic-correctional mental health services. Urine drug screening detects substance use and deters the entry of contraband into forensic-correctional units, albeit with limitations. For example, a point-of-care urine sample may not be possible and patients can alter or substitute samples to avoid detection, highlighting the role of ancillary tools to detect contraband substances. This study describes the pattern and types of substances detected from environmental samples using a gas chromatographic analyzer (TeknoScan TSI3000) in forensic-correctional populations to model the benefits of similar tools in similar settings. Samples collected over 18 months (January 2020 to June 2021) by trained staff members using the machine were reviewed. During this period, 217 environmental samples were recorded, and 66 (30%) samples were positive for contraband substances, including tetrahydrocannabinol (25%), methamphetamines (19%), and cocaine (16%). Other substances detected include methylene-dioxymethamphetamine, heroin, morphine, lysergic acid diethylamide, tramadol, and methyl-benzoate. Fewer positive samples were detected, especially during the time corresponding with the COVID-19 restriction on the forensic units. TeknoScan was beneficial as an ancillary tool to detect and deter contraband substances. It also provided evidence for risk management. Adequate training is needed for the successful implementation of the tool.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".