Improving the legibility of prescription medication labels for older adults and adults with visual impairment
Bibliographic record
Abstract
OBJECTIVES: Most current prescription labels fail to meet print guidelines, especially in print size. We therefore compared the legibility of current prescription medication labels against the legibility of prototype labels, based on current guidelines for legibility. METHOD: Sample medication labels were obtained from pharmacies, and prototype medication labels were developed according to legibility guidelines from nongovernmental organizations and pharmacy organizations. Three groups of participants, consisting of older adults with normal vision, older adults with visual impairment and younger adults with visual impairment (total N = 71) took part. Participants were asked to read and rank the labels. Reading speed and accuracy were determined. RESULTS: Accuracies were high (75%-100%), and there were no significant differences between samples or prototypes or between groups. Prototypes, however, were read faster than samples (p < 0.001). Subjectively, participants preferred the largest print option (p < 0.001) and instructions with the numbers written in highlighted uppercase words (p < 0.001). DISCUSSION: The results indicate that improvements to the label would include larger print size, a consistent layout with left justification and using upper case with highlighting for emphasis of the numbers in the instructions.
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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".