A Scoping Review on the Use of Infographics as a Health-Related Knowledge Translation Tool
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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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 | 0.021 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".