Testing for cannabis in the work‐place: a review of the evidence
Bibliographic record
Abstract
BACKGROUND: Urinalysis testing in the work-place has been adopted widely by employers in the United States to deter employee drug use and promote 'drug-free' work-places. In other countries, such as Canada, testing is focused more narrowly on identifying employees whose drug use puts the safety of others at risk. AIMS: We review 20 years of published literature on questions relevant to the objectives of work-place drug testing (WPDT), with a special emphasis on cannabis, the most commonly detected drug. RESULTS: We conclude (i) that the acute effects of smoking cannabis impair performance for a period of about 4 hours; (ii) long-term heavy use of cannabis can impair cognitive ability, but it is not clear that heavy cannabis users represent a meaningful job safety risk unless using before work or on the job; (iii) urine tests have poor validity and low sensitivity to detect employees who represent a safety risk; (iv) drug testing is related to reductions in the prevalence of cannabis positive tests among employees, but this might not translate into fewer cannabis users; and (v) urinalysis has not been shown to have a meaningful impact on job injury/accident rates. CONCLUSIONS: Urinalysis testing is not recommended as a diagnostic tool to identify employees who represent a job safety risk from cannabis use. Blood testing for active tetrahydrocannabinol (THC) can be considered by employers who wish to identify employees whose performance may be impaired by their cannabis use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".