Use of infographics as a health-related knowledge translation tool: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Efforts to bridge the know-do gap have paved the way for development of the field of knowledge translation (KT). KT aims to understand how evidence use can best be promoted and supported through different activities. For dissemination activities, infographics are gaining in popularity as a promising KT tool to reach multiple health research users (eg, health practitioners, patients and families, decision-makers). However, to our knowledge, no study has yet mapped the available evidence on this tool using a systematic method. This scoping review will explore the depth and breadth of evidence on infographics use and its effectiveness in improving research uptake (eg, raising awareness, influencing attitudes, increasing knowledge, informing practice and changing behaviour). METHODS AND ANALYSIS: , and further refined by the Joanna Briggs Institute (2020). The search will be conducted in MEDLINE, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, Social Science Abstracts, Library and Information Science Abstracts, Education Resources Information Center, Cairn and Google Scholar. We will also search for relevant literature from the reference lists of the included publications. Two independent reviewers will select the studies. All study designs will be eligible for inclusion, with no date or publication status restrictions. The included studies will have evaluated infographics that disseminate health research evidence and target a non-scientific audience. A data extraction form will be developed and used to extract and chart the data, which will then be synthesised to present a descriptive summary of the results. ETHICS AND DISSEMINATION: Ethics approval is not required. To inform the research and KT communities, various dissemination activities will be developed, including user-friendly KT tools (eg, webinars, fact sheets and infographics), open-access publication and presentations at KT events and conferences.
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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 | 0.125 | 0.140 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.103 | 0.025 |
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".