549 The Quality of TikTok Videos in the Education, Prevention, and Management of Burn Injuries
Bibliographic record
Abstract
Abstract Introduction Most burn injuries are preventable, and appropriate first aid post-injury results in improved outcomes for the patient as well as a reduced burden on the healthcare system. Many patients turn to internet resources for education and management approaches for their burn injuries. With the rising popularity of social media platforms for the promotion of health information, this creates a more accessible educational realm for those who may not know where to otherwise access informational resources. However, most content on social media is not validated or reliable. Previous studies have investigated the quality of online burn prevention, education, and management videos on platforms such as YouTube. In this study, we attempt to assess the quality of such videos on TikTok, a newer and rapidly growing platform. Methods Videos on TikTok were searched for using 27 keywords (hashtags) such as #burn, #education, #prevention, #management, and #firstaid. The first 30 videos for each hashtag were reviewed. The Global Quality Scale (GQS) was used to assess the quality of the videos that fell within the inclusion criteria. Metrics such as views, viewer engagement, commentary, and likes were also examined. Results Of 525 reviewed videos, 72 met inclusion criteria. 47.2% (n=34) were on first aid or medical management, 33.4% (n=31) were on basic information, and 9.7% (n=7) were on prevention. Only 5.6% (n=4) videos cited sources. The mean GQS score was 2.45. 50% of videos (n=36) were made by creators with medical training, and of those videos, the GQS score was 3.13, whereas content made by creators with no medical training had a mean GQS of 1.81 (p < 0.001). The excluded videos (n=453) fell into 6 main categories: unrelated (n=273), burn wounds (n=106), personal experiences (n=49), advertisements (n=8), questions (n=2), and non-English videos (n=15). Viewership varied from 41 to 4.2 million views. Conclusions Overall, information regarding burns on TikTok is unsatisfactory. Additionally, viewers are more drawn to lower quality videos. While quality varies amongst the different sources and is dependent on the creator, there is a significant gap in reliable content regarding burns, demonstrating an opportunity for validated content creation in this realm. Patients must exert caution and be selective when using TikTok as an educational resource regarding burns. Applicability of Research to Practice This review addresses key gaps in the knowledge base on TikTok for reliable burn information. This will later be used as a framework to create a series of educational videos for 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 | 0.003 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".