The impact of social media promotion with infographics and podcasts on research dissemination and readership
Bibliographic record
Abstract
Objective: In 2015 and 2016, the Canadian Journal of Emergency Medicine (CJEM) Social Media (SoMe) Team collaborated with established medical websites to promote CJEM articles using podcasts and infographics while tracking dissemination and readership. Methods: CJEM publications in the "Original Research" and "State of the Art" sections were selected by the SoMe Team for podcast and infographic promotion based on their perceived interest to emergency physicians. A control group was composed retrospectively of articles from the 2015 and 2016 issues with the highest Altmetric score that received standard Facebook and Twitter promotions. Studies on SoMe topics were excluded. Dissemination was quantified by January 1, 2017 Altmetric scores. Readership was measured by abstract and full-text views over a 3-month period. The number needed to view (NNV) was calculated by dividing abstract views by full-text views. Results: Twenty-nine of 88 articles that met inclusion were included in the podcast (6), infographic (11), and control (12) groups. Descriptive statistics (mean, 95% confidence interval) were calculated for podcast (Altmetric: 61, 42-80; Abstract: 1795, 1135-2455; Full-text: 431, 0-1031), infographic (Altmetric: 31.5, 19-43; Abstract: 590, 361-819; Full-text: 65, 33-98), and control (Altmetric: 12, 8-15; Abstract: 257, 159-354; Full-Text: 73, 38-109) articles. The NNV was 4.2 for podcast, 9.0 for infographic, and 3.5 for control articles. Discussion Limitations included selection bias, the influence of SoMe promotion on the Altmetric scores, and a lack of generalizability to other journals. Conclusion: Collaboration with established SoMe websites using podcasts and infographics was associated with increased Altmetric scores and abstract views but not full-text article views.
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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 | 0.044 | 0.249 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".