Cannabis use motives and associations with personal and work characteristics among Canadian workers: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Research on cannabis use motives has focused on youth. Little is known about motives among working adults, including how work may play a role. This study aimed to describe cannabis use motives and their connection to work, and identify the personal and work correlates of work-related motives among a sample of workers. METHODS: A national, cross-sectional sample of Canadian workers were queried about their cannabis use. Workers reporting past-year cannabis use (n = 589) were asked their motives for using cannabis and whether each motive was related to work or helped them manage at work (i.e., work-related). Multinomial logistic regression analyses were conducted to estimate the associations of personal and work characteristics with work-related cannabis use motives (no work-related motives, < 50% of motives work-related, ≥ 50% of motives work-related). RESULTS: Use for relaxation (59.3%), enjoyment (47.2%), social reasons (35.3%), coping (35.1%), medical reasons (30.9%), and sleep (29.9%) were the most common motives. Almost 40% of respondents reported one or more of their cannabis use motives were work-related, with coping (19.9%) and relaxation (16.3%) most commonly reported as work-related. Younger age, poorer general health, greater job stress, having a supervisory role, and hazardous work were associated with increased odds of reporting at least some cannabis use motives to be work-related, while work schedule and greater frequency of alcohol use were associated with reduced odds of motives being primarily work-related. CONCLUSIONS: Cannabis use motives among workers are diverse and frequently associated with work. Greater attention to the role of work in motivating cannabis use is warranted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".