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Record W2026494463 · doi:10.1177/002204260503500411

Urine Collection Jars versus Video Games: Perceptions of Three Stakeholder Groups toward Drug and Impairment Testing Programs

2005· article· en· W2026494463 on OpenAlexaff
Gerard Seijts, Grace O’Farrell

Bibliographic record

VenueJournal of Drug Issues · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsUniversity of WinnipegWestern University
Fundersnot available
KeywordsPerceptionStakeholderSAFEREnthusiasmPsychologyDrugDrug detectionApplied psychologyHarmWork (physics)Substance Abuse DetectionCounterproductive work behaviorSocial psychologyPublic relationsPolitical scienceComputer scienceComputer securityPsychiatryEngineering

Abstract

fetched live from OpenAlex

The use of drug testing in the workplace is a controversial practice. Scholars, practitioners, unions, and organizations have therefore begun to explore whether there are alternative approaches to reduce counterproductive behaviors at work. We investigated the perceptions of labor relations experts, drivers of transportation vehicles, and users of public transportation services toward drug and impairment testing programs in the workplace. Impairment testing was viewed as more favorable in terms of combating and controlling counterproductive behaviors at work than drug testing. Perceptions of fairness, effectiveness in detecting impaired performance, and the potential to enhance a safer working environment were higher in the impairment testing condition as compared to the drug testing condition. Perceived invasiveness was lower in the impairment testing condition relative to the drug testing condition. Labor relations experts showed the least enthusiasm for both drug testing and impairment testing programs.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.065
GPT teacher head0.323
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations4
Published2005
Admission routes1
Has abstractyes

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