Geriatric nursing (New York, N.Y.) · 2026
Content quality and source credibility of fall prevention videos for older adults on Chinese short-video platforms: A cross-sectional content analysis.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Liu Peng, Wang Wenjing, Wang Fuzhi, Yang Changzheng
Аннотация
BACKGROUND: Falls constitute a leading cause of injury-related mortality among older adults globally. Chinese short-video platforms collectively reach over 900 million users, presenting unprecedented opportunities for health education, but the quality of fall prevention content and its relationship with user engagement have not been systematically evaluated.
OBJECTIVE: To evaluate fall prevention video quality across major Chinese short-video platforms, identify content creator characteristics associated with higher-quality information, and examine whether user engagement metrics correlate with video quality.
METHODS: We conducted a cross-sectional analysis of 216 fall prevention videos from five platforms (Douyin, Kuaishou, Bilibili, Xiaohongshu, Xigua Video) during October-November 2025. Two independent medical-school graduates with formal medical education and research expertise in medical informatics assessed video quality using the modified DISCERN instrument (mDISCERN; range 5-25) and Global Quality Scale (GQS; range 1-5). Interrater agreement was quantified using both intraclass correlation coefficients (ICC) and Cohen's weighted κ. User engagement metrics were extracted and analyzed using both Pearson and Spearman correlations.
RESULTS: Interrater reliability was excellent for mDISCERN (ICC=0.890; weighted κ=0.890) and good for GQS (ICC=0.723; weighted κ=0.722). Mean mDISCERN score was 17.61 (SD 2.87), with 48.1% achieving high quality. Uploader type demonstrated the strongest quality association (ε²=0.64): healthcare professionals substantially outperformed self-media creators (Cohen d=3.42). Platform verification strongly predicted quality (88.7% vs 9.1% high-quality; φ=0.79). Engagement metrics showed weak association with quality in this sample (Spearman ρ=0.149 for likes, explaining only 2.2% of variance), with detection power constrained by severe right-skewness and floor effects (e.g., 30.1% of videos had zero comments).
CONCLUSIONS: Content creator credentials and platform verification effectively discriminate video quality, while engagement metrics show only weak association in this sample. These findings support platform policies prioritizing verified professional content and indicate that engagement-based metrics, despite their algorithmic prominence, do not reliably signal health information quality in this dataset.
OBJECTIVE: To evaluate fall prevention video quality across major Chinese short-video platforms, identify content creator characteristics associated with higher-quality information, and examine whether user engagement metrics correlate with video quality.
METHODS: We conducted a cross-sectional analysis of 216 fall prevention videos from five platforms (Douyin, Kuaishou, Bilibili, Xiaohongshu, Xigua Video) during October-November 2025. Two independent medical-school graduates with formal medical education and research expertise in medical informatics assessed video quality using the modified DISCERN instrument (mDISCERN; range 5-25) and Global Quality Scale (GQS; range 1-5). Interrater agreement was quantified using both intraclass correlation coefficients (ICC) and Cohen's weighted κ. User engagement metrics were extracted and analyzed using both Pearson and Spearman correlations.
RESULTS: Interrater reliability was excellent for mDISCERN (ICC=0.890; weighted κ=0.890) and good for GQS (ICC=0.723; weighted κ=0.722). Mean mDISCERN score was 17.61 (SD 2.87), with 48.1% achieving high quality. Uploader type demonstrated the strongest quality association (ε²=0.64): healthcare professionals substantially outperformed self-media creators (Cohen d=3.42). Platform verification strongly predicted quality (88.7% vs 9.1% high-quality; φ=0.79). Engagement metrics showed weak association with quality in this sample (Spearman ρ=0.149 for likes, explaining only 2.2% of variance), with detection power constrained by severe right-skewness and floor effects (e.g., 30.1% of videos had zero comments).
CONCLUSIONS: Content creator credentials and platform verification effectively discriminate video quality, while engagement metrics show only weak association in this sample. These findings support platform policies prioritizing verified professional content and indicate that engagement-based metrics, despite their algorithmic prominence, do not reliably signal health information quality in this dataset.