A review of workplace substance use policies in Canada. Strengths, gaps and key considerations.
Bibliographic record
Abstract
Substance use policies and practices in the workplace are at a relatively early stage, and research and information in this area is limited. There are many areas where improved knowledge and understanding could be beneficial for various stakeholders, particularly employers and employees. Given these facts, the objectives of this study were: • To review, analyze and provide a general overview of the state of workplace policies on substance use in Canada, their common components and unique elements, and any gaps; • To identify lessons learned and best practices in developing and implementing workplace substance use policies from the experiences of safety-sensitive organizations; and • To determine which policy areas require more guidance, tools and resources, and from this information make recommendations to help improve policy development and employer responses to substance use affecting the workplace. This study is the first of its kind to explore the state of Canadian substance use policies in the workplace. It is primarily intended for employers and human resources professionals interested in developing or improving workplace policies and best practices related to substance use, and secondarily for other professionals working with organizations (e.g., medical professionals, SAPs/SAEs, lawyers, etc.).
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.026 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".