Infographics and/or Pictograms and Medication Adherence: A Scoping Review
Bibliographic record
Abstract
Introduction: Medication adherence is the act of following medication instructions from a health care provider. Infographics and pictograms are visual science communication tools that have been shown to improve medication adherence. Objective: To synthesize and critically evaluate literature surrounding the use of infographics and pictograms in medication adherence. Methods: We conducted a literature search on PubMed, Ovid MEDLINE, CINAHL, Web of Science, and PsycInfo with the purpose of identifying literature published between the years 2000 and 2022. Primary research articles were included for quantitative analysis if they explored/reported the following topics and/or outcomes: (a) infographics and/or pictograms as the exposure of interest and (b) adherence, comprehension, or health outcomes as the outcome measures. Results: 30 studies were included in the results. Outcome measures assessed included (a) comprehension and understanding of factors surrounding medication adherence, (b) medication adherence, and (c) health outcomes. Our review of the studies showed that 87.5% of studies measuring outcome (a), 78.2% of those measuring outcome (b), and 100% of those measuring outcome (c) found improvements when using infographics or pictograms. Conclusion: Our review supports the use of infographics and pictograms as a means of improving medication adherence among a diverse set of demographics, illnesses, and treatments. Practice Implications: Infographics and pictograms are useful tools to improve medication adherence. When these tools are designed carefully, they increase the accessibility of medication information in a wide range of patient populations.
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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 | 0.011 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".