Pictographic instructions for medications: do different cultures interpret them accurately?
Bibliographic record
Abstract
Abstract Objective (1) To determine whether a range of linguistically diverse individuals of non-European descent could understand the meaning of predeveloped pictograms without any additional aids such as verbal explanations; (2) to identify appropriate modifications to the pictograms to diminish errors of interpretation; and (3) to test the notion that central pictogram elements have the same meaning irrespective of language or culture. Setting The study was conducted in Vancouver, Canada. Method This study tested a set of 16 predeveloped pictograms to determine whether they accurately communicated the intended medication administration directions to participants who neither speak nor read English, French or Spanish. Both qualitative and quantitative methods evaluated the pictograms' interpretability among 39 participants from three language groups, Cantonese, Somali and Punjabi. Standard analysis of variance tested for differences due to language groups and other demographics. Key findings Only four of the 16 initial pictograms tested were interpreted correctly by 70% of participants. Relaxing the criterion from 70% to 50% included seven more. Modifications of problem elements within the pictograms further improved interpretation accuracy levels by 22%, to a final accuracy of 67.15%. Quantity errors were twice as common as timing, administration route or auxiliary instruction errors. Conclusions Participants could identify particular pictographic symbols they found confusing or ambiguous. Basic education and time since immigration predicted interpretation accuracy better than first language or any other demographic characteristic.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".