Ophthalmology on social networking sites: an observational study of Facebook, Twitter, and LinkedIn
Bibliographic record
Abstract
BACKGROUND: The use of social media in ophthalmology remains largely unknown. Our aim was to evaluate the extent and involvement of ophthalmology journals, professional associations, trade publications, and patient advocacy and fundraising groups on social networking sites. METHODS: An archived list of 107 ophthalmology journals from SCImago, trade publications, professional ophthalmology associations, and patient advocacy organizations were searched for their presence on Facebook, Twitter, and LinkedIn. Activity and popularity of each account was quantified by using the number of "likes" on Facebook, the number of followers on Twitter, and members on LinkedIn. RESULTS: Of the 107 journals ranked by SCImago, 21.5% were present on Facebook and 18.7% were present on Twitter. Journal of Community Eye Health was the most popular on Facebook and JAMA Ophthalmology was most popular on Twitter. Among the 133 members of the International Council of Ophthalmology, 17.3% were present on Facebook, 12.8% were present on Twitter, and 7.5% were present on LinkedIn. The most popular on Facebook was the International Council of Ophthalmology, and the American Academy of Ophthalmology was most popular on Twitter and LinkedIn. Patient advocacy organizations were more popular on all sites compared with journals, professional association, and trade publications. Among the top ten most popular pages in each category, patient advocacy groups were most active followed by trade publications, professional associations, and journals. CONCLUSION: Patient advocacy groups lead the way in social networking followed by professional organizations and journals. Although some journals use social media, most have yet to engage its full potential and maximize the number of potential interested individuals.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".