P072: The effect of infographic promotion on research dissemination and readership: a randomized control trial
Bibliographic record
Abstract
Introduction: With the increasing volume of medical literature published each year, it is difficult for clinicians to translate the latest research into practice. Awareness is the first step of knowledge translation and journals have begun using social media to increase the dissemination and awareness of their publications. Infographics can describe research findings visually, are shared broadly on social media, and may be a more effective way to convey information. We hypothesized that infographic abstracts would increase the social media dissemination and online readership of research articles relative to traditional abstracts. Methods: In this randomized controlled trial, 24 original research articles were chosen from the six issues of the Canadian Journal of Emergency Medicine (CJEM) published between July 2016 and May 2017 (4 articles per issue). Half were randomized to the infographic and control groups within each issue. Infographic articles were promoted using a visual infographic outlining the findings of the article. Control articles were promoted using a screen capture image of each articles abstract. Both were disseminated through the journals social media accounts (Twitter and Facebook) along with the link to the selected article. Infographics were also published on CanadiEM.org. Abstract views, full text views, and the change in Altmetric score were tracked for 30 days and compared between groups. Unpaired two-tailed t-tests were used to detect significant differences. Results: Abstract views (mean, SD) were significantly higher for infographic articles (378.9, 162.0) than control articles (175.5, 69.2, p<0.001). Mean Altmetric scores were significantly higher for infographic articles (26.4, 13.8) than control articles (3.4, 1.7, p<0.0001). There was no statistically significant difference in full-text views between infographic (49.7, 90.4) and control articles (25.3, 12.3). Conclusion: CJEM articles promoted on social media using infographics had higher abstract viewership and Altmetric scores than those promoted with traditional abstracts. Although there was no difference in full-text readership, our results suggest that infographic abstracts may have a role in increasing the dissemination of medical literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".