Evaluating the Content and Quality of Videos Related to Hypertrophic Scarring on TikTok in China: Cross-Sectional Study
Classification
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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Hypertrophic scars (HTSs) are a predominant condition after burns and trauma, and it causes severe physiological and psychological problems. TikTok (Douyin in Chinese), a popular platform for sharing short videos, has shown the potential to spread health information, including information related to HTSs. Educating the public to obtain correct information is important to reduce the incidence of physiological and psychological problems caused by HTSs. However, the quality and reliability of HTS-related video content on TikTok in mainland China have not been thoroughly studied. OBJECTIVE: This study aims to evaluate the content and quality of short videos related to HTSs on the Chinese version of TikTok (Douyin) and explore the factors related to their quality, providing valuable insights for health information dissemination. METHODS: We collected a sample of 153 TikTok videos in Chinese related to HTSs and categorized them according to video source and content. We evaluated the video content using a coding schema, and a hexagonal radar schema was used to intuitively display the spotlight and weight of each aspect of the videos. We evaluated quality using 4 standardized tools: the modified DISCERN (mDISCERN) questionnaire, the Journal of the American Medical Association, the Global Quality Scale (GQS), and the Health on the Net Foundation Code of Conduct. We also explored the potential relationship between video quality and characteristics. RESULTS: The analysis showed that health care professionals uploaded all videos about treating HTSs, which matched the hexagonal radar model analysis findings. The quality assessment scores for the Journal of the American Medical Association, GQS, mDISCERN, and the Health on the Net Foundation Code of Conduct had median values of 1 (IQR 1-2), 2 (IQR 2-3), 2 (IQR 2-3), and 3 (IQR 3-4), respectively, indicating a need to improve the quality and reliability of videos on HTSs. In addition, high-quality videos were more popular, based on metrics such as likes, comments, favorites, and shares (P<.001). Interestingly, the time when the videos were uploaded positively correlated with GQS and mDISCERN scores (r=0.393; P<.001 and r=0.273; P<.001), while the video length did not significantly correlate with evaluation scores (P=.78, P=.20, P=.07, and P=.04). CONCLUSIONS: The quality of TikTok videos related to HTSs is generally moderate. Users should exercise caution when seeking information on HTSs from TikTok. It is advisable to choose videos uploaded by health care professionals from the burn department and the burn plastic surgery department, and in the Chinese context, those produced in first-tier cities and emerging first-tier cities.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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