ADD A LANGUAGE! ADD A PICTURE!—IMPROVING PRESCRIPTION MEDICATION LABELS FOR ELDERLY SINGAPOREANS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it