ORIGINAL RESEARCH How Many Words Does a Picture Really Tell? Cross-sectional Descriptive Study of Pictogram Evaluation by Youth
Bibliographic record
Abstract
Background: Communicating health-related instructions with pictograms is useful, but such graphics can be interpreted in different ways. It is crucial to understand which pictogram components are best for accurate communication. Objectives: To catalogue pictograms used to label drugs in clinical practice; to identify the common graphic elements for defined categories of pictograms, by performing a semiotic analysis (studying how signs are perceived and how they should be designed); to identify the key graphic elements common to pictograms preferred by users; and to develop suggestions for future pictogram design on the basis of users ’ input. Methods: Literature and Internet searches were performed to identify pictograms and pictogram categories. A call for pictograms was also circulated through the International Pharmaceutical Federation (FIP). Youth at a Canadian pediatric hospital were asked to rate pictograms
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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 | 0.003 | 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) | 0.007 | 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".