Understanding of Prescription Medical Labels as a Function of Age, Culture, and Language
Bibliographic record
Abstract
Symbols are used to convey how and when to take medication and a number of dangers with use that should be avoided. Symbols also have the potential to convey important information to people despite differences in culture and language. This study evaluated 10 prescription medication labels under consideration for use in the Canadian medical system. The sample of 238 participants was composed of a number of subgroups; namely, from a university, a Chinese student association, English as a second language (ESL) courses, low literacy classes, and a senior citizens centre. Symbols that conveyed time or multiple concepts, such as how often to take a drug each day or over the course of a week, were poorly understood. Elderly, ESL, and Chinese groups had the lowest levels of comprehension. Other pictorials such as “do not take with alcohol” and “shake well before using” achieved ISO (International Organisation for Standardisation) recommended comprehension levels (i.e., 67%). Discussion centers on comprehension difficulties with certain labels, group differences, and the importance of heterogenous sampling.
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 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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".