Pictograms: Can They Help Patients Recall Medication Safety Instructions?
Bibliographic record
Abstract
Objectives: The effectiveness of pictograms to enhance the recall of information through a review of the literature was evaluated. Methodology: A search was conducted using "Pictogram" AND "Recall" on PubMed, SCOPUS, and Web of Knowledge databases. Additional searches were conducted on the above-mentioned databases and on Google Scholar using various combinations of key words "pictorial", "picture", "aid", "memory" and "medication". The main inclusion criterion was recall measurement. Results: Nineteen articles were analyzed. Ten studies measured immediate/short-term recall; five compared immediate/short-term to long-term recall; and four measured only long-term recall. Eight measured cued recall of pictograms and eleven measured free recall. Three studies failed to support pictograms as means to enhance recall for all subjects regardless of demographic characteristics. Recall abilities of elderly participants were lower than young individuals. Literacy level, education level, prior knowledge, and cultural familiarity are factors that may influence pictogram recall. Conclusion: Pictograms enhance patients' recall of information. Professionals using pictograms in healthcare settings should consider 1) educating patients about pictograms; 2) providing patients with pictorial cues; 3) measuring free recall with "true" method; 4) assessing patient's reading, education level, and prior knowledge of pictograms; 5) using text and pictograms; 6) and having special considerations for the elderly.
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 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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.009 |
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".