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.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".