Medication Adherence and the Role of Pictograms in Medication Counselling of Chronic Patients: a Review
Bibliographic record
Abstract
Pharmaceutical care requires a patient-centered approach, focusing on the ability of patients to understand drug-related information and follow the instructions delivered by pharmacists as well as other health-care providers included in the circle of care. With the goal of ensuring the prescribed use of medications, called medication adherence, health-care providers have to consider many risk factors such as geography (culture), social economic status, age, and low literacy that may predispose patients to non-adherence, and considerations have to be made for chronic patients living with life-long disease states. The aim of this review is to provide a balanced and comprehensive review outlining a number of different medication counselling and education approaches that have been used to try to improve medication adherence and health outcomes with the use of clear and concise graphic illustrations-called pictograms. By highlighting the current landscape of the general use and efficacy of pharmaceutical pictograms to aid in the knowledge and recall of drug-related information, as well as outlining specific medication adherence outcomes with pharmaceutical pictograms in chronic patients, the current review describes the need for health-care providers to move beyond the traditional didactic methods of oral and verbal communication with patients regarding medication-taking behavior.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".