Short-form video sharing platforms as a source of information for children's growing pains: A cross-sectional content...

To evaluate the quality, reliability, and educational value of short-form videos pertaining to children's growing pains on popular social media platforms (TikTok, Rednote, Bilibili, and YouTube).

A cross-sectional analysis of 200 short-form videos (50 per platform) was conducted using standardized search terms. Video quality was assessed using four validated instruments: modified DISCERN (mDISCERN), the Global Quality Scale (GQS), the Video Information and Quality Index (VIQI), and the Patient Education Materials Assessment Tool (PEMAT). Metadata and user engagement metrics were collected, and statistical analyses included descriptive statistics, group comparisons, and correlation analyses.

TikTok demonstrated superior performance compared to other platforms in reliability (mDISCERN: 3.00 (3.00, 4.00); GQS: 4.00 (3.00, 4.00); median (IQR); p < 0.001), educational value (PEMAT-Understandability: 80.00 (66.70, 92.22); PEMAT-Actionability: 80.00 (60.00, 80.00); median (IQR); p < 0.001), and content comprehensiveness (VIQI-Total score: 14.00 (12.25, 15.00); median (IQR); p < 0.001). Videos created by healthcare professionals showed significantly higher quality scores and more comprehensive clinical content coverage. User engagement metrics such as likes, comments, video duration, and followers showed positive correlations with several video quality scores (r: 0.12-0.56, p < 0.05). However, engagement alone should not be considered a definitive indicator of quality.

In conclusion, while short-form videos represent a valuable educational resource for parents, their quality varies significantly across platforms and creators. Content from healthcare professionals, particularly on TikTok, was found to be more reliable and robust. This underscores the critical role of platform algorithms in quality curation. Future initiatives should therefore encourage professional creator participation and optimize recommendation systems to prioritize informational accuracy.