Design, Development and Evaluation of Pictographic Instructions for Medications Used during Humanitarian Missions
Bibliographic record
Abstract
Objectives: To develop, design and evaluate pictographic instructions for medications used during humanitarian medical missions. Methods: We developed and designed pictographic medication instructions based on the storyboard pictogram concept and then evaluated the pictograms during an itinerant medical mission in Gabon. We evaluated patients' comprehension of both the individual pictograms and the storyboard template at the time of dispensing or at short-term follow-up. Patients were asked questions to assess their comprehension of the indication, dose, route of administration, frequency and auxiliary instructions. Demographic data, including age, sex and mother tongue, were also collected. During the follow-up interview, we assessed the patients' understanding and recall of the pictographic instructions. Results: Pictograms were tested with 525 patients at the time of dispensing; 47 of these patients were also seen at short-term follow-up (the day after dispensing). Most of the pictograms tested achieved the European Commission standard for comprehension of greater than 80%, but were slightly below the American National Standard Institute criterion of greater than 85% comprehension. The use of pictograms caused a moderate increase in workload for the health care providers. Conclusions: The use of a pictographic storyboard was valuable for medication counselling in a humanitarian aid setting; however, the use of pictograms increased the workload of the health care providers who supplied the counselling.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".