A Scoping Review on the Use of Infographics as a Health-Related Knowledge Translation Tool
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Infographics are gaining in popularity as a promising knowledge translation (KT) tool to reach multiple health research users. This scoping review explores the depth and breadth of empirical evidence available on infographics' use and its effectiveness. A systematic search was conducted across MEDLINE, CINAHL, PsycInfo, Social Science Abstracts, ERIC, Cairn, Google Scholar, and Google Web. Articles were screened and abstracted independently by two reviewers. Among the 2173 sources identified, 21 met inclusion criteria. Of the included studies, 71% were published since 2018, 76% were conducted in North America, and 22% addressed cancer prevention. A great diversity in research designs and indicators is observed. Most studies used self-reported questionnaires often administered post-intervention. In general, infographics are appreciated, considered visually appealing, perceived as useful and easy to understand. According to experimental studies identified, infographics would not be more effective than other tools for information acquisition and retention, intention to act, and behavior change, except for specific subgroups. However, more studies are necessary to better understand the added value of infographics for knowledge translation compared to other dissemination tools, considering different target audiences and types of knowledge, and to identify characteristics (e.g., structure, message framing) that may influence their impact.
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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 | 0.016 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 it