Evaluating the Use of Pictograms for the Improvement in Patient Understanding of Medication Instruction–A Pilot Study
Bibliographic record
Abstract
The first step in ensuring patient compliance is to ensure that the patient understands how to use their medication. The aim of the study was to determine if patients understanding of medication use can be improved by using pictograms. A simple randomized control test was set up whereby 50 patients were selected to participate, of which 25 people were included in the test group to receive medication labels based on pictograms as well as the usual counselling and 25 people were included in the control group and received the usual labelling of their medication and counselling. Questionnaires were used to determine the patient’s demographic information and health literacy on the day of inclusion into the study, while another questionnaire was completed 3 to 7 days later to determine patient understanding of how to use their medication and if they used their medication correctly. At a 95% confidence interval and p<0.05 there is a relationship between improved understanding and the use of pictograms. Pictograms improve patient’s understanding of how to use their medication, which in turn lead to an improvement in adherence.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".