Understanding Social Media Use and Engagement Among Dermatology Patients to Inform Dermatological Prevention and Care in Vietnam: Cross-sectional Study
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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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".