Evidence-based infographics and visual communication as powerful tools to improve health outcomes
Bibliographic record
Abstract
Abstract Background Anemia can double the risk of death in pregnancy, with women in developing countries being the most at risk. Adherence to an iron supplementation regimen can markedly improve overall health outcomes for those who are affected by iron deficiency anemia. Still, most adherence-boosting programs require significant counseling time with a primary care provider, a resource that is becoming increasingly scarce. In our review of the literature, we explored infographics and other visual science communication tools for their ability to improve medication adherence, with a special focus on low literacy populations in the developing world. With the results of this review being favourable, we conducted a subsequent literature search on factors that can amplify the useful effect of infographics. Methods We conducted a literature search on PubMed, Ovid MEDLINE, CINAHL, Web of Science, and PsycInfo to identify literature on effective visual science communication techniques. Techniques from the literature were then compiled and sorted into the categories of “DOs” or “DON’Ts” of developing impactful infographics. These guidelines were then utilized to develop an infographic depicting an iron supplementation regimen for pregnant women. Results 27 recommendations on developing effective and impactful infographics were identified from the literature. These included, but were not limited to, the following: utilize accompanying text that is understandable at a low literacy level to prevent misinterpretation of the message depicted through pictogram(s), and use a culturally-sensitive lens to design images that would be easily relatable among the target cultural demographic. These recommendations were adapted to develop an evidence-based infographic. Conclusions Our review of effective visual science communication techniques supports the importance of using evidence-based methods to develop infographics that are effective among a range of demographics and medical conditions. Key messages • Evidence-based infographics and visual science communication tools can amplify the effectiveness of science and health communication. • Infographics and visual science communication tools can be applied to a variety of literacy levels and medical conditions to improve patient outcomes.
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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 | 0.015 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".