A Review and Quality Assessment of TikTok Videos for Burn Education
Bibliographic record
Abstract
Social media platforms can serve as a readily accessible tool for burn education, potentially reducing the incidence and severity of burn injuries. Previous studies have investigated the quality of online burn education videos on platforms such as YouTube. Here, we review the quality of such videos on TikTok, a newer and rapidly growing platform. TikTok was searched for English videos using 29 keywords (hashtags) such as #burn, #education, #prevention, and #management. The first 30 videos per hashtag were screened. Videos were categorized by content and creator. Two independent reviewers assessed the quality of the included videos using the Global Quality Scale (GQS). Metrics such as views, commentary, and likes were also examined. Of 535 screened videos, 72 met inclusion criteria. 47% (n = 34) were on management, 33% (n = 31) education, and 10% (n = 7) prevention. Only 6% (n = 4) cited sources. The median GQS score was 3.0 (IQR: 2.0-3.0, max 4.0). 50% (n = 36) were made by healthcare professionals with a median GQS score of 3.0 (IQR: 2.0-3.0, max 4.0) compared to 2.0 (IQR: 2.0-3.0, max 4.0) in nonhealthcare professionals (n = 36). Viewership varied from 41 to 4.2 million views. Overall, there is a lack of high-quality educational information on TikTok. This rapidly expanding and dynamic platform may provide an opportunity to direct individuals to higher quality resources.
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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 | 0.022 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.024 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".