Understanding Social Media Use and Engagement Among Dermatology Patients to Inform Dermatological Prevention and Care in Vietnam: Cross-sectional Study
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Bibliographic record
Abstract
Background Social media has emerged as a common source of dermatological information. Monitoring the patterns of social media use and engagement is important to counteract the limitations of social media. However, evidence in Vietnamese dermatology patients is lacking. Objective This study aimed to explore social media use and engagement by dermatology patients and to identify factors associated with social media use and engagement. Methods A cross-sectional study was conducted with 519 participants at the Vietnam National Hospital of Dermatology and Venereology during September to November 2018. Data about sociodemographic characteristics, social media use, and social media engagement were collected. Multivariate logistic and tobit regression models were used to identify factors associated with social media use and engagement. Results Interest in information about “cosmetic, beauty, and skincare techniques” was the greatest (184/519, 46.2%). The mean engagement score was 8.4 points (SD 2.4 points). Female patients were more likely to use social media (odds ratio [OR] 2.23, 95% CI 1.23-4.06) and be interested dermatological information on social media (OR 3.09, 95% CI 1.35-7.09). Women also had higher social media engagement scores (coefficient=0.68, 95% CI 0.17-1.18). Higher social media engagement scores were related with Instagram use (coefficient=0.58, 95% CI 0.00-1.15) and higher credibility scores for “family members” (coefficient=0.15, 95% CI 0.03-0.26) and “dermatology companies” (coefficient=0.22, 95% CI 0.04-0.39). Conclusions This study discovered high social media usage among dermatology patients. However, only moderate utilization and credibility levels were reported regarding the use of social media as a source of dermatological information. More efforts should focus on involving dermatologists in the development of individualized information on social media targeting specific groups of dermatology patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it