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Record W4411234727 · doi:10.1080/15389588.2025.2513396

Drug prevalence in Canadian driving population

2025· article· en· W4411234727 on OpenAlexaffabout
Cynthia Coulter, J. Gonzales, D J Beirness, Emma Beecroft, Craig A. Harper, Jarrad R. Wagner, Christine Moore

Bibliographic record

VenueTraffic Injury Prevention · 2025
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsYukon Health and Social Services
Fundersnot available
KeywordsPoison controlInjury preventionHuman factors and ergonomicsOccupational safety and healthSuicide preventionPopulationMedical emergencyForensic engineeringEnvironmental healthEngineeringGeographyTransport engineeringMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: In 2024 a drug prevalence roadside survey was performed in the Yukon territory of Canada. Volunteer drivers on Wednesday through Saturday nights June through August 2024 were asked to donate oral fluid samples and participate in a questionnaire of drug use. Samples were collected from 294 noncommercial and 220 commercial drivers. Oral fluid sample collection was chosen due to the ease of collection for the donor. Drugs in oral fluid are indicative of those compounds in the blood at the time of collection. Drugs are deposited in oral fluid by diffusion from blood or coating the oral mucosa. Studies have shown similar drug class results when oral fluid and blood are compared (Kelley-Baker et al. 2014). The objective of this paper is to perform additional screening by liquid chromatography time of flight mass spectrometry (LC-QTOF-MS) on all samples for the Tier I and II compounds as suggested by NSC-ADID recommendations. METHODS: collection device and shipped overnight to 9 Delta Analytical, LLC where they were screened for delta 9-tetrahydrocannabinal, amphetamines, cocaine, benzodiazepines, and opiates including fentanyl. Initial screening was performed using a liquid chromatograph tandem mass spectrometer (LC-MS/MS). Screening parameters were fully validated and published in a peer reviewed journal (Coulter et al. 2022). All samples screening positive were confirmed by LC-MS/MS using a second sample aliquot. RESULTS: Additional testing resulted in an increased positivity rate for both driving cohorts. Positivity rates increased to 25% for both groups with polydrug use seen in 7.5% and 4.5% for noncommercial and commercial drivers respectively. Statistical analysis showed THC concentrations were different between the two driving groups. The mean concentration for THC in noncommercial drivers was 29 ng/mL compared to 8 ng/mL for commercial drivers, when high concentration outliers were removed. Additional discoveries of over the counter and antidepressant medication were made using Tier I and II recommendations. CONCLUSION: The NSC-ADID Tier I and II recommendations should be followed when conducting drug prevalence surveys of drivers. LC-QTOF-MS is the recommended tool to conduct drug screening for oral fluid samples.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.028
GPT teacher head0.401
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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