Preliminary survey of leading general medicine journals’ use of Facebook and Twitter
Bibliographic record
Abstract
Aim: This study is the first to chart the use of Facebook and Twitter by peer-reviewed medical journals. Methods: We selected the top 25 general medicine journals on the Thomson Reuters Journal Citation Report (JCR) list. We surveyed their Facebook and Twitter presences and scanned their Web sites for any Facebook and (or) Twitter features as of November 2011. Results/Discussion: 20 of 25 journals had some sort of Facebook presence, with 11 also having a Twitter presence. Total ‘Likes’ across all of the Facebook pages for journals with a Facebook presence were 321,997, of which 259, 902 came from the New England Journal of Medicine (NEJM) alone. The total numbers of Twitter ‘Followers’ were smaller by comparison when compiled across all surveyed journals. ‘Likes’ and ‘Followers’ are not the equivalents of total accesses but provide some proxy measure for impact and popularity. Those journals in our sample making best use of the open sharing nature of social media are closed-access; with the leading open access journals on the list lagging behind by comparison. We offer a partial interpretation for this and discuss other findings of our survey, provide some recommendations to journals wanting to use social media, and finally present some future research directions. Conclusions: Journals should not underestimate the potential of social media as a powerful means of reaching out to their readership.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".