Perceptions of Medical Cannabis Packaging and Labeling among Middle-Aged and Older Canadians
Bibliographic record
Abstract
Abstract The use of cannabis for therapeutic purposes is becoming more popular in many countries, including the United States and Canada. In Canada, middle-aged and older adults make up the largest proportion of medical cannabis users. Canadian legislation mandates that medical cannabis be packaged in plain-looking containers with small labels, childproof caps, and required health warnings. This is meant to standardize the way cannabis products are distributed, as well as protect children from accidental ingestion. However, there is limited research on how these regulations affect cannabis users over age 45. In the present study, residents of Winnipeg, Manitoba, Canada aged 45 and older (n=40) were surveyed regarding their experiences with medical cannabis packaging and labeling. Half of the participants (50%) felt they had a hard time opening their medical cannabis container. A majority (60%) thought having an easy-open lid would be helpful. Most participants (78%) reported experiencing difficulties reading the label on their container, and 75% thought it would be helpful to have a printout of the label in a larger font. In addition, 89% of participants who took more than one kind of medical cannabis favored a symbol on their medication bottle that would indicate the type of medical cannabis contained inside. Implications for policy makers and future research are discussed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".