The legibility of prescription medication labelling in Canada
Bibliographic record
Abstract
INTRODUCTION: The legibility of medication labelling is a concern for all Canadians, because poor or illegible labelling may lead to miscommunication of medication information and poor patient outcomes. There are currently few guidelines and no regulations regarding print standards on medication labels. This study analyzed sample prescription labels from Ontario, Canada, and compared them with print legibility guidelines (both generic and specific to medication labels). METHODS: Cluster sampling was used to randomly select a total of 45 pharmacies in the tri-cities of Kitchener, Waterloo and Cambridge. Pharmacies were asked to supply a regular label with a hypothetical prescription. The print characteristics of patient-critical information were compared against the recommendations for prescription labels by pharmaceutical and health organizations and for print accessibility by nongovernmental organizations. RESULTS: More than 90% of labels followed the guidelines for font style, contrast, print colour and nonglossy paper. However, only 44% of the medication instructions met the minimum guideline of 12-point print size, and none of the drug or patient names met this standard. Only 5% of the labels were judged to make the best use of space, and 51% used left alignment. None of the instructions were in sentence case, as is recommended. DISCUSSION: We found discrepancies between guidelines and current labels in print size, justification, spacing and methods of emphasis. CONCLUSION: Improvements in pharmacy labelling are possible without moving to new technologies or changing the size of labels and would be expected to enhance patient outcomes.
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 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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".