The effect of an infographic promotion on research dissemination and readership: A randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: Journals use social media to increase the awareness of their publications. Infographics show research findings in a concise and visually appealing manner, well suited for dissemination on social media platforms. We hypothesized that infographic abstracts promoted on social media would increase the dissemination and online readership of the parent research articles. METHODS: Twenty-four articles were chosen from the six issues of CJEM published between July 2016 and June 2017 and randomized to infographic or control groups. All articles were disseminated through the journal’s social media accounts (Twitter and Facebook). Control articles were promoted using a screen capture image of each article’s abstract on the journal’s social media accounts. Infographic articles were promoted similarly using a visual infographic. Infographics were also published and promoted on the CanadiEM.org’s website and social media channels. Abstract views, full-text views, and the change in Altmetric score were compared between groups using unpaired two-tailed t-tests. RESULTS: There were no significant differences in the groups at baseline. Abstract views (mean, 95% CI) were higher in the infographics (379, 287-471) than the control group (176, 136-215, p<0.001). Mean change in Altmetric scores was higher in the infographics (26, 18-34) than in the control group (3, 2-4, p<0.0001). There was no difference in full-text views between the infographics (50, 0-101) and control groups (25, 18-32). CONCLUSION: The promotion of CJEM articles using infographics on social media and the CanadiEM.org website increased Altmetric scores and abstract views. Infographics may have a role in increasing awareness 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 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 | 0.034 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".