ADD A LANGUAGE! ADD A PICTURE!—IMPROVING PRESCRIPTION MEDICATION LABELS FOR ELDERLY SINGAPOREANS
Bibliographic record
Abstract
In Singapore, medication labels placed by clinics on packets/bottles of dispensed prescription medications are primarily in English. This poses a challenge for elderly Singaporeans (≥65 years) as 61% of them cannot read in English. However, nearly half of them can read in ≥1 of the other three official languages (Chinese/Malay/Tamil), thus suggesting a potential strategy, i.e., adding another language, for improving prescription medication labels. Pictograms, shown to be helpful for low-literacy populations elsewhere, are another potential strategy. We assessed the utility of these strategies, i.e., bilingual labels and/or labels with pictograms, in improving the understanding of medication labels among elderly Singaporeans. Respondents were randomized to 4 different label types - (A) English-text (n=357); (B) English-text with pictograms (n=357); (C) Bilingual-text (n=353); and (D) Bilingual-text with pictograms (n=350) - for the same three medications, and questioned on their understanding of the label content. While 65% of those randomized to Type A reported difficulty reading the labels, corresponding proportions were significantly lower with the addition of pictograms and/or another language (57%, 32%, 37%, for types B, C, D, respectively). However, even among those able to read English, 12%, 14%, 10% and 9%, respectively across each label type still reported difficulty. Use of bilingual medication labels is a promising strategy for improving prescription medication labels for elderly Singaporeans. However, careful assessment of the label design and content and of non-label-related factors that may limit the elderly’s understanding is warranted. Our findings can support future empirical studies evaluating real-world prescription medication labels for elderly Singaporeans.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".