Impact of a Physician-Led Social Media Sharing Program on a Medical Journal’s Web Traffic
Bibliographic record
Abstract
PURPOSE: The use of social media by health professionals and medical journals is increasing. The aim of this study was to compare online views of articles in press (AIPs) released by Annals of Emergency Medicine before and after a nine-person social media team started actively posting links to AIPs using their personal Twitter accounts. METHODS: An observational before-and-after study was conducted. Web traffic data for Annals were obtained from the publisher (Elsevier), detailing the number of page views to annemergmed.com by referring websites during the study period. The preintervention time period was defined as January 1, 2013, to June 30, 2014, and the postintervention period as July 1, 2014, to July 31, 2015. The primary outcome was page views from Twitter per AIP released each month to account for the number of articles published each month. Secondary outcomes included page views from Facebook (on which there was no article-sharing intervention) and total article views per month. RESULTS: The median page views from Twitter per individual AIP released each month increased from 33 in the preintervention period to 130, for an effect size of 97 (95% confidence interval, 56-111; P < .001). There was a smaller increase in median page views from Facebook per individual AIP of 21 (95% confidence interval, 10-32). There was no significant increase in these median values for total page views per AIP. CONCLUSIONS: Twitter sharing of AIPs increased the number of page views that came from Twitter but did not increase the overall number of page views.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".