The evaluation of pharmaceutical pictograms among elderly patients in community pharmacy settings – a multicenter pilot study
Bibliographic record
Abstract
Piotr Merks,1,2,* Damian Åwieczkowski,3,* Marcin Balcerzak,4 Ewelina Drelich,4 Katarzyna BiaÅoszewska,5 Natalia Cwalina,3 Jerzy Krysinski,1 MiÅosz Jaguszewski,3 Annie Pouliot,6 Regis Vaillancourt6 1Department of Pharmaceutical Technology, Collegium Medicum in Bydgoszcz, Nicolaus University in Torun, Bydgoszcz-Torun, Poland; 2Piktorex Sp. z.o.o., Warsaw, Poland; 3First Department of Cardiology, Medical University of Gdansk, Gdansk, Poland; 4Farenta Polska, Warsaw, Poland; 5Department of Medical Psychology, Medical University of Warsaw, Warsaw, Poland; 6Children’s Hospital of Eastern Ontario, Ottawa, ON, Canada *These authors contributed equally to this work Introduction: The search for new ways to optimize the use of medications by patients has led the pharmaceutical community to promote the idea of introducing pictograms into routine practice. The main intention of pictograms is to ease patient adherence and to reduce potential risks or errors associated with the use of medications. Purpose: To evaluate a series of pharmaceutical pictograms for patient comprehension. Patients and methods: The study was conducted in community pharmacies within a European Union country that belongs to the professional research network. Structured interviews were used to evaluate the pictograms for patient comprehension. This consisted of an assessment of the following: the transparency and translucency of the pictograms, health literacy, and pictogram recall. Participants were also given the opportunity to provide feedback on how to improve the pictograms. The primary endpoint was pictogram comprehension. Secondary outcomes included recall of the pictograms and pictogram translucency. Results: The study included 68 patients with whom face-to-face interviews were performed. Low transparency results (≤25%) and extensive patient feedback in initial interviews led to the withdrawal of certain pictograms (n=15) from the evaluation. Among the pictograms included in the final stage of our research, 22 pictograms (62.8%) obtained an acceptable transparency level ≥66%. All pictograms passed the short-term recall test with positive results. Conclusion: A majority of the designed and modified pictograms reached satisfactory guessability scores. Feedback from patients enabled modification of the pictograms and proved that patients have an important voice in the discussion regarding the design of additional pictograms. Keywords: pharmaceutical pictograms, elderly population, health literacy, community pharmacy, Poland
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".