Ophthalmology Practice and Social Media Influences: A Patients Based Cross-Sectional Study among Social Media Users
Bibliographic record
Abstract
Many physicians consider social media a good tool for building their brands and attracting patients. However, limited data exist on patients’ perceptions of the value of social media in ophthalmology. Therefore, our objective was to examine how social media influences patients when choosing an ophthalmologist among social media users, and people’s behaviors toward ophthalmologists’ social media accounts. This was a cross-sectional study including 1086 participants. Males represented 77.3% of the sample. The majority of the participants (71.3%) were aged between 25 and 54 years. Regarding social media sites frequently checked, Twitter ranked first (75.3%), followed by Snapchat (52.8%) and YouTube (48.7%). The majority (92.3%) used social media sites at all times of the day. Concerning the importance of ophthalmologists’ social media sites, around 36.3% considered it either very or extremely important. As regards the important factors about an ophthalmologist’s social media site from participants’ perspectives, medical information written by the ophthalmologist (45.5%) and recommendations by friends (45.4%) were the most common reasons. Around 21% of females, compared to 16.8% of males, perceived the ophthalmologists’ social media sites as extremely important, p = 0.041. A quarter of participants aged between 18 and 24 years, compared to only 5.5% of those aged 65 and above, perceived the ophthalmologists’ social media sites as extremely important, p = 0.018. In conclusion, a considerable proportion of the people who used social media described ophthalmologists’ social media sites as very/extremely important in their choice of an ophthalmologist.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".