Quality and Reliability Evaluation of Mechanical Ventilation-Related Video Content on Short-Video Platforms: A Cross-Sectional Study

Quality and Reliability Evaluation of Mechanical Ventilation-Related Video Content on Short-Video Platforms: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quality and Reliability Evaluation of Mechanical Ventilation-Related Video Content on Short-Video Platforms: A Cross-Sectional Study Qingmei Fan, Lingling Huang, Xin Jing, Lulu Tang, Zhenzhen Gu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9224693/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Mechanical ventilation is a core life-support technology in intensive care units. Since the COVID-19 pandemic, the public's demand for science popularization of such medical knowledge has risen significantly. Short-video platforms have become important carriers for health science popularization, yet the quality of relevant health popularization content varies greatly, and there remains a lack of systematic evaluation on the quality and reliability of mechanical ventilation-related videos. Objective This study aimed to systematically evaluate the quality, reliability and completeness of mechanical ventilation-related videos on three major short-video platforms: Bilibili, TikTok and WeChat Channels. Methods A cross-sectional study was conducted in November 2025 to analyze 157 mechanical ventilation-related videos from the three major Chinese short-video platforms. The Global Quality Scale (GQS), modified DISCERN (mDISCERN) and a self-designed completeness scale were used to assess the quality, reliability and completeness of the videos. Meanwhile, user interaction indicators including video duration, number of likes, comments and favorites were collected, and statistical analyses were performed to explore the correlations among various video variables. Results A total of 157 mechanical ventilation-related videos were included. The median scores of GQS, mDISCERN and completeness were 3 (interquartile range, IQR: 3–3), 3 (IQR: 2–3) and 2 (IQR: 1–3) respectively, with only 3.2% of the videos containing complete content. Significant inter-platform differences were observed: Bilibili had the optimal content completeness ( P < 0.05), and its GQS score was comparable to that of WeChat Channels and significantly higher than that of TikTok( P < 0.01); TikTok showed the highest mDISCERN score, user interaction level and proportion of certified publishers ( p < 0.01). The scores of videos released by professionally certified publishers were significantly higher than those by non-certified ones ( P < 0.05). Positive correlations were found between GQS and mDISCERN scores, between video duration and GQS score, and between the number of likes and mDISCERN score (all P < 0.05). Conclusion Mechanical ventilation science popularization videos on the three short-video platforms were of moderate overall quality with insufficient content completeness, and significant differences existed across the platforms: Bilibili had better content completeness, TikTok featured higher user interaction and a larger proportion of certified publishers, and WeChat Channels had acceptable reliability but poor content completeness. Videos released by professionally certified publishers were of higher quality. It is necessary to strengthen platform regulation and professional review to improve the quality of medical science popularization content. Short video Mechanical ventilation Bilibili TikTok WeChat Channels Figures Figure 1 Figure 2 Figure 3 Introduction Mechanical ventilation is a core life-support technology in intensive care units, providing essential respiratory support for critically ill patients such as those with respiratory failure. It plays an irreplaceable role in clinical scenarios including critical illness management and perioperative respiratory care [ 1 , 2 ]. Since the global spread of the COVID-19 pandemic, the clinical application of respiratory support measures (e.g., oxygen therapy and mechanical ventilation) has garnered widespread public attention, and the demand for accessible and comprehensible medical knowledge related to mechanical ventilation has increased significantly [ 3 – 5 ]. With the rapid advancement of the Internet and new media technologies, social media and online video platforms have become vital carriers for medical knowledge dissemination. Among them, short-video platforms represented by Bilibili, TikTok, and WeChat Channels have emerged as important and primary channels for the public to obtain health and medical information, as well as key venues for health science popularization, owing to their characteristics of rapid dissemination, intuitive presentation, and broad user coverage [ 6 – 8 ]. Social media is increasingly utilized by patients to self-educate on disease processes and to identify hospitals, physicians, and physician networks with optimal expertise for their conditions. Patients are no longer passive consumers of health information; instead, they play an active role in the delivery of health services through online environments. However, the regulation of health information sources on such platforms remains a major challenge, leading to inconsistent information quality overall. Misleading or inaccurate health information can not only be detrimental to patients but also erode public trust in physicians and undermine doctor-patient relationships [ 9 ]. In China, the Healthy China 2030 Planning Outline explicitly prioritizes "strengthening health education to improve national health literacy" and emphasizes "utilizing internet information technologies to conduct targeted health science popularization"[ 10 ], which provides a strategic direction for new media platforms to develop high-quality health science content [ 11 ]. Despite this guidance, short-video platforms still face notable shortcomings in its implementation. Low entry thresholds, the absence of standardized professional review mechanisms, and inadequate content quality control have resulted in highly variable quality of health science content on these platforms [ 12 ], with unvalidated medical information and fragmented professional knowledge spreading widely [ 13 ]. As a highly specialized critical medical technology that integrates multidisciplinary knowledge and has strict clinical operational requirements, the accuracy, completeness, and reliability of mechanical ventilation popular science content directly shape public cognition. Biased or incomplete content not only fails to achieve the goals of science popularization but may also mislead public understanding of clinical medical practices, thereby disrupt doctor-patient communication and even influence clinical decision-making [ 14 ]. To scientifically evaluate the quality of online health information, researchers commonly adopt the Global Quality Scale (GQS) [ 15 ]to assess the overall scientificity and production quality of videos, and the well-validated modified DISCERN (mDISCERN) tool [ 16 ] to evaluate the reliability of video such content. In recent years, these two tools have been widely applied in the quality evaluation of short videos focused on various medical topics, including sleep disorders [ 17 ], liver cancer [ 18 ], chronic renal failure [ 19 ], and uraemia [ 20 ]. Nevertheless, there is currently a lack of systematic evaluation on the quality and reliability of mechanical ventilation popular science content—a core technology in critical care medicine—on Chinese short-video platforms. Therefore, this study adopted a cross-sectional research design to analyze mechanical ventilation-related videos on three major Chinese short-video platforms (Bilibili, TikTok, and WeChat Channels). We systematically assessed the content quality, reliability, and completeness of these videos, clarified the current status and existing problems of mechanical ventilation science popularization via short videos, and aimed to provide empirical evidence for regulating the dissemination of medical popular science content on short-video platforms and optimizing the health science popularization ecosystem, thereby facilitating the implementation of targeted health science popularization practices. Materials and Methods Search Strategy and Data Collection This study adopted a cross-sectional research design. From November 10 to November 11, 2025, relevant video samples were collected by searching three major mainstream Chinese short-video platforms (Bilibili, TikTok, and WeChat Channels). The search was performed using the single Chinese keyword “机械通气” (mechanical ventilation) . To minimize bias caused by personalized recommendation algorithms, a new account was created and logged into on each short-video platform, and searches were conducted on both the web and mobile app interfaces. The search was not restricted by video release date, and all publicly available videos matching the keyword were included in the initial screening. Prior to data collection, inclusion and exclusion criteria were defined. Inclusion criteria: (1) The video language is Chinese; (2) The popular science content focuses on mechanical ventilation. Exclusion criteria: (1) Duplicate videos; (2) Videos unrelated to "mechanical ventilation" or merely news reports; (3) Silent videos; (4) Non-Chinese videos. To ensure the consistent application of these criteria, the following steps were implemented: duplicate content was identified by comparing video titles and core information; relevance was independently evaluated by two researchers based on thematic focus; and advertising materials were excluded based on commercial intent. All exclusions were reviewed, and any discrepancies were resolved by a third researcher to reach a consensus. The following characteristics of the retrieved videos were extracted and analyzed: video duration, number of likes, number of comments, number of favorites, video platform, release date, publisher's certification status, and video content theme. Video data were extracted within 24 hours after searching to minimize changes in engagement indicators over time. All data were recorded using Microsoft Excel. Video Evaluation Video quality was independently assessed using two validated instruments: the GQS and the modified DISCERN tool. Two registered nurses with more than 5 years of work experience in intensive care units served as reviewers. Both possessed practical experience in the clinical application of mechanical ventilation, received unified training on the scales, and proficiently mastered the scoring criteria of GQS and modified DISCERN. The two reviewers independently performed information extraction and quality scoring of the videos; for videos with controversial scores, a respiratory therapist acted as a third researcher for review, and the final score was determined through consultation among the three researchers. GQS is a widely used video scoring tool for evaluating the quality of health information presented in videos. It is a 5-point scale, with a score of 1 indicating poor quality and limited utility, and a score of 5 indicating excellent quality, clarity, comprehensiveness, and informativeness. The modified DISCERN tool is used to assess the reliability of videos, encompassing five domains: clarity, reliability, content balance, other information sources, and future perspectives. Scores range from 0 to 5 points (6 grades in total) based on "yes" or "no" responses, with higher scores indicating stronger content reliability. Additionally, a self-designed completeness scoring system was developed: combining the professional knowledge system of mechanical ventilation, content completeness was evaluated from four core dimensions—concepts and principles, indications, modes and core parameters with waveform interpretation, and prevention and complications. Each dimension was scored as "not mentioned (0 points)", "partially explained (1 point)", or "fully explained (2 points)", with a total score ranging from 0 to 8 points. Higher scores indicate better content completeness. Ethical Considerations This study does not require ethical approval as it only analyzes publicly accessible short videos and does not involve clinical data, human specimens, animal experiments, or personal privacy information. Statistical Analysis IBM SPSS 31.0 and R 4.5.1 software were used for statistical analysis. First, a normality test was performed on all data, and the results indicated that all data in this study followed a non-parametric distribution. Therefore, continuous variables were expressed as median (interquartile range) [M (IQR)], and categorical variables were presented as frequency (percentage) [n (%)]. The chi-square test was used for comparing categorical variables among multiple groups, and Fisher's exact test was employed when the theoretical frequency was < 5. The Mann-Whitney U test was used for comparing continuous variables between two groups, and the Kruskal-Wallis H test was applied for comparing continuous variables among three or more groups. Spearman's rank correlation analysis was conducted to explore correlations among various video variables, and Cohen's kappa coefficient was used to assess the consistency of scores between the two reviewers. A kappa value > 0.75 indicates good consistency. A p value < 0.05 was considered statistically significant. Results Video Screening A total of 320 videos were collected as initial screening samples through keyword search on the three short-video platforms (Bilibili, TikTok, and WeChat Channels), including 221 on Bilibili, 65 on TikTok, and 34 on WeChat Channels. According to the inclusion and exclusion criteria, 65 duplicate videos, 74 irrelevant videos, 9 silent videos, and 15 non-Chinese videos were excluded sequentially. Finally, 157 videos were included for subsequent analysis, with 94 on Bilibili (59.9%), 32 on TikTok (20.4%), and 31 on WeChat Channels (19.7%). The video inclusion process is illustrated in Fig. 1 . Basic Characteristics of Videos Among the 157 included videos, 23 (14.6%) were released by professionally certified personnel, and 134 (85.4%) were released by non-professionally certified individuals. The median video duration was 1139 seconds (IQR: 247–2262), and there was a statistically significant difference in video duration among the platforms. User interaction indicators showed a highly skewed distribution, with a median of 27 likes (IQR: 9–72), 1 comment (IQR: 0–4), and 75 favorites (IQR: 21–225). The results of video quality and completeness scoring revealed a median completeness score of 2 points (IQR: 1–3), a median GQS score of 3 points (IQR: 3–3), and a median mDISCERN score of 3 points (IQR: 2–3). The consistency test of scores between the two reviewers showed that the Cohen's kappa values for GQS, mDISCERN, and completeness scores were 0.80, 0.82, and 0.81, respectively, indicating good consistency between the two reviewers' ratings. The basic characteristics of the videos are presented in Table 1 . Table 1 Basic characteristics of videos Characteristics N = 157 Short-video platform, [n (%)] Bilibili 94(59.9) TikTok 32(20.4) WeChat Channels 31(19.7) Video source, [n (%)] Professionally certified personnel 23(14.6) Non-professionally uncertified personnel 134(85.4) Video duration [seconds, median (IQR)] 1139(247–2262) Number of likes [median (IQR)] 27(9–72) Number of comments [median (IQR)] 1(0–4) Number of favorites [median (IQR)] 75(21–225) Completeness score [median (IQR)] 2(1–3) GQS score [median (IQR)] 3(3–3) mDISCERN score [median (IQR)] 3(2–3) Short-Video Content Analysis Analysis of the completeness of the four core dimensions of the included videos showed that the overall completeness of mechanical ventilation-related popular science content was poor, with only 3.2% of the videos able to fully elaborate on all four core dimensions. Among them, 84.1% of the videos only partially mentioned or did not mention concepts/principles, 66.2% did not reference indications-related content, 78.3% failed to cover the prevention and complications dimension, and only the mode/parameters/waveform dimension had a relatively high full explanation rate of 38.2% (Table 2 ). A comparison of content completeness among platforms revealed that the video content completeness on Bilibili was significantly superior to that on TikTok and WeChat Channels. The median score for the mode/parameters/waveform dimension was 2 (IQR:1–2), which was significantly higher than that on TikTok[1 (IQR:0–2)] and WeChat Channels [1 (IQR:0–1)] ( H = 27.072, p < 0.001); the median total completeness score was 3 (IQR:2–3), which was higher than that on TikTok[2 (IQR:2–3)] and WeChat Channels [1 (IQR:1–3)] ( H = 7.495, p = 0.024); no statistically significant differences were observed in the concepts/principles, indications, and prevention and complications dimensions among the three platforms ( p > 0.05) ((see Table 3 , Fig. 2 ). Table 2 Analysis of video content completeness Video content Not mentioned (0 points) Partially explained (1 point) Fully explained (2 points) Concepts/principles, n (%) 61 (38.9) 71 (45.2) 25 (15.9) Indications, n (%) 102 (66.2) 42 (26.8) 11 (7.0) Modes/parameters/waveforms, n (%) 28 (17.8) 69 (43.9) 60 (38.2) Prevention and complications, n (%) 123 (78.3) 29 (18.5) 5 (3.2) Table 3 Comparison of video content completeness scores among different short-video sharing platforms Content dimension Bilibili (n = 94) Median [IQR] TikTok(n = 32) Median [IQR] WeChat Channels (n = 31) Median [IQR] H value P value Concepts/principles 1 [0–1] 0 [0–1] 1 [0–1] 3.129 0.209 Indications 0 [0–1] 0 [0–1] 0 [0–1] 1.831 0.400 Modes/parameters/waveforms 2 [ 1 – 2 ] 1 [0–2] 1 [0–1] 27.072 < 0.001*** Prevention and complications 0 [0–0] 0 [0–1] 0 [0–0] 2.081 0.353 Total completeness score 3 [ 2 – 3 ] 2 [ 2 – 3 ] 1 [ 1 – 3 ] 7.495 0.024* Footnote: Kruskal-Wallis H test was used; * p < 0.05, *** p < 0.001. Video Quality and Reliability Evaluation The overall quality and reliability of the included videos were at a moderate level, with a median GQS score of 3 points (IQR: 3–3) and a median mDISCERN score of 3 points (IQR: 2–3) (see Table 1 ). Significant differences in video quality and reliability were observed among the three platforms: the GQS scores of Bilibili and WeChat Channels were similar and significantly higher than that of TikTok (p < 0.01); the mDISCERN scores of TikTok and WeChat Channels were not significantly different and both significantly higher than that of Bilibili ( p = 0.01) (see Table 4 ). A comparison of user interaction and publisher certification status among platforms showed that Bilibili had the longest video duration [2044 seconds (IQR:1214–2791)] but the lowest user interaction, with the number of likes [15 (IQR:7–38)] and comments [1 (IQR:0–3)] significantly lower than those of TikTok( p < 0.01); TikTok had the strongest user interaction, with a median of 91 likes (IQR:25–1574) and 5 comments (IQR:0–40), both the highest among the three platforms ( p 0.05). Chi-square test results indicated a significant association between video platform and publisher's certification status (χ²=38.914, p < 0.001). Bilibili had the highest proportion of uncertified publishers (97.9%), TikTok had the highest proportion of professionally certified publishers (46.9%), and WeChat Channels had 80.6% of uncertified publishers (Table 4 ). A comparison of video quality between professionally certified and non-certified publishers showed that the mDISCERN score of certified publishers [3 (IQR:2–3)] was significantly higher than that of non-certified publishers [3 (IQR:3–4)] ( U = 2246.0, p < 0.01), and the completeness score of the mode/parameters/waveform dimension [1 (IQR:1–2)] was also significantly higher than that of non-certified publishers [1 (IQR:0–2)] ( U = 1172.0, p = 0.047); no statistically significant differences were observed in GQS score, total completeness score, and other dimension scores between the two groups ( p > 0.05) (see Table 5 , Fig. 3 ). Table 4 Comparison among different short-video sharing platforms Variables Bilibili (n = 94) Median [IQR] TikTok(n = 32) Median [IQR] WeChat Channels (n = 31) Median [IQR] P value Video duration (seconds) 2044 [1214–2791] 197 [96–343] 215 [137–440] < 0.001*** Number of likes 15 [7–38] 91 [25–1574] 27 [12–67] < 0.01** Number of comments 1 [0–3] 5 [0–40] 0 [0–2] < 0.01** Number of favorites 85 [31–228] 59 [15–1206] 57 [13–169] 0.150 GQS score 3 [ 3 – 3 ] 3 [ 2 – 3 ] 3 [ 3 – 3 ] < 0.01** mDISCERN score 3 [ 2 – 3 ] 3 [ 3 – 3 ] 3 [ 3 – 3 ] 0.010* Total completeness score 3 [ 2 – 3 ] 2 [ 2 – 3 ] 2 [ 1 – 3 ] 0.024* Video source, [n (%)] < 0.001*** Uncertified 92 (97.9) 17 (53.1) 25 (80.6) Certified 2 (2.1) 15 (46.9) 6 (19.4) Footnote: All continuous variables were non-normally distributed and expressed as median [interquartile range]. * p < 0.05, ** p < 0.01, *** p < 0.001. Table 5 Comparison of videos between certified and non-certified accounts Variables Non-certified (n = 134) Median [IQR] Certified (n = 23) Median [IQR] U value P value GQS score 3 [ 3 – 4 ] 3 [ 3 – 3 ] 1606.5 0.696 mDISCERN score 3 [ 3 – 4 ] 3 [ 2 – 3 ] 2246.0 < 0.01** Total completeness score 2 [ 2 – 3 ] 2 [ 2 – 3 ] 1498.5 0.826 Concepts/principles 1 [0–1] 1 [0–1] 1553.0 0.948 Indications 1 [0–1] 0 [0–1] 1834.0 0.080 Modes/parameters/waveforms 1 [0–2] 1 [ 1 – 2 ] 1172.0 0.047* Prevention and complications 0 [0–0] 0 [0–0] 1530.0 0.939 Footnote: Mann-Whitney U test was used; * p < 0.05, ** p < 0.01. Correlation Analysis Spearman's rank correlation analysis results demonstrated a significant positive correlation between video GQS score and mDISCERN score (r = 0.391, p 0.05); the number of likes was significantly positively correlated with mDISCERN score (r = 0.200, p = 0.012), but no significant correlation with GQS score ( p > 0.05); no significant correlations were found between the number of comments or favorites and GQS score or mDISCERN score ( p > 0.05) (see Table 6 ). Table 6 Correlations between video GQS score, mDISCERN score, and user interaction indicators (Spearman’s rank correlation analysis) Variables GQS Score mDISCERN Score r P value r P value mDISCERN Score 0.391 < 0.001*** - - Video Duration (seconds) 0.198 0.013* -0.144 0.072 Number of Likes -0.013 0.873 0.200 0.012* Number of Comments -0.081 0.311 0.118 0.140 Number of Favorites 0.125 0.118 0.121 0.132 Footnote: Spearman’s rank correlation analysis; * p < 0.05, *** p < 0.001. Discussion This study systematically evaluated the quality, reliability, and completeness of mechanical ventilation-related popular science videos on three major Chinese short-video platforms (Bilibili, TikTok, and WeChat Channels). The results indicated that these videos were of moderate overall quality, with suboptimal reliability and a notable lack of content completeness. Significant inter-platform differences in content characteristics were observed, and videos released by professionally certified publishers demonstrated distinct advantages in reliability and partial dimension completeness. These findings are consistent with the results of short-video quality evaluation studies in other medical fields such as amblyopia [ 21 ] and colorectal polyps [ 22 ], reflecting the common challenges of popularizing highly professional medical technologies on short-video platforms. Current Status and Underlying Causes of Video Quality and Reliability The median GQS and mDISCERN scores of the included videos in this study were both 3 points, indicating an overall moderate to low level of quality and reliability, and only 14.6% of the videos were released by professionally certified personnel—a proportion much lower than the professional participation rate in the popularization of common diseases knowledge [ 18 , 19 ]. This situation primarily stems from the low entry thresholds of short-video platforms, which allow non-professional individuals to freely publish medical-related content without strict review. Moreover, the review mechanisms of most short-video platforms lack the participation of professional medical personnel, making it difficult to ensure the accuracy and scientificity of highly professional content such as mechanical ventilation knowledge. Meanwhile, relevant videos on platforms such as Bilibili are mostly recordings of professional medical lectures. Although such content possesses high professionalism and scientificity, it lacks popularized translation and simplified explanation, making it difficult for the general public to understand and accept. In contrast, content created by non-professional creators is prone to knowledge biases and even errors due to the lack of systematic medical training. These two factors have jointly contributed to the relatively low overall quality and reliability scores of mechanical ventilation popular science videos on short-video platforms [ 23 – 25 ]. Core Issues of Video Content Completeness This study found that the completeness of mechanical ventilation popular science videos was generally poor, with only 3.2% of the videos able to fully cover the four core dimensions. The lack of information in the prevention and complications, and indications dimensions was the most severe, which is similar to the findings of studies on lung cancer popular science short videos [ 26 ]. The inherent time constraint of short videos is a key contributing factor: creators struggle to comprehensively and systematically elaborate on interdisciplinary mechanical ventilation knowledge within a limited video duration, and thus often choose relatively intuitive and easy-to-present content such as ventilation modes and core parameters for creation while neglecting core clinical information such as clinical indications and complication prevention [ 27 ]. In addition, non-professional creators lack systematic critical care medicine knowledge and can only compile and share fragmented information obtained from the internet; even popular science content created by professional medical personnel is mostly fragmented due to the pursuit of short and concise expression adapted to short-video platforms, leading to insufficient overall content completeness. Such serious information gaps may not only hinder the public's comprehensive and accurate understanding of mechanical ventilation but also potentially lead to misunderstandings of clinical medical practices and even incorrect health decision-making. Impact of Platform Heterogeneity and Publisher Qualifications The significant differences in video quality, user interaction, and the proportion of certified publishers among the three platforms are closely related to the inherent user positioning and dissemination mechanisms of each platform [ 28 ]. Bilibili is positioned as a platform for knowledge sharing and professional content dissemination, thus achieving the optimal video content completeness among the three platforms. However, the relatively academic professional nature of its content leads to low user interaction and an extremely low proportion of professionally certified publishers. TikTok has a huge and diverse user base covering all age groups and education levels, and its mature Blue V professional certification mechanism has attracted more professional creators to join, resulting in the strongest user interaction among the three platforms. Nevertheless, due to the platform's emphasis on content interestingness and short duration, the professionalism and comprehensiveness of mechanical ventilation popular science content are insufficient. WeChat Channels is a short-video platform based on social circle communication, which exhibits obvious fragmented content characteristics due to its strong social attributes, with both content quality and reliability at a moderate level. Furthermore, this study confirmed that videos by professionally certified publishers are significantly superior to those by non-certified ones in terms of reliability and the completeness of ventilation modes/parameters/waveforms dimension. However, there is no significant difference in overall quality (GQS score) and total content completeness score between the two groups. This indicates that the current identity certification mechanisms of short-video platforms only verify the professional qualifications of creators but do not evaluate or regulate the quality and completeness of their published popular science content. Some content released by certified publishers still remains fragmented and overly academic, failing to meet the popular science needs of the general public [ 29 ]. Correlation between Video Variables and User Interaction Indicators This study identified a significant positive correlation between GQS score and mDISCERN score, suggesting that the higher the video quality of mechanical ventilation popular science videos, the relatively stronger the content reliability [ 30 ]. Video duration was positively correlated with GQS score, indicating that longer videos are more likely to achieve detailed content elaboration and better production quality, but have no significant correlation with reliability which may be due to the fact that reliability is more dependent on the professional background of creators rather than video length. The number of likes was only weakly correlated with mDISCERN score, and the number of comments and favorites had no significant correlation with video quality and reliability scores. This result is consistent with relevant studies on premature ovarian failure [ 25 ] and atherosclerosis [ 30 ], indicating that user interaction indicators on short-video platforms are more influenced by non-professional factors such as content interestingness and dissemination popularity, and cannot truly reflect the professional quality and scientificity of the content [ 31 , 32 ]. Meanwhile, the strong professionalism and low public awareness of mechanical ventilation led to a relatively narrow target audience, resulting in relatively low overall interaction data of such videos [ 33 ]. This also suggests that creators need to balance video duration, professional content quality and user-friendly presentation to improve the dissemination effect and popular science value of mechanical ventilation videos [ 34 ]. Potential and Development Direction of Health Science Popularization on Short-Video Platforms Despite the numerous challenges in current mechanical ventilation short-video popularization, the inherent advantages of short-video platforms—such as rapid dissemination and wide coverage—make them important carriers for critical care medicine knowledge popularization [ 35 ]. Based on the results of this study, future optimization and improvement of mechanical ventilation health science popularization on short-video platforms should focus on three key aspects: first, strengthen the professional review mechanism of short-video platforms, establish a specialized professional medical review team composed of critical care physicians, respiratory therapists and nursing specialists, and improve the accuracy and scientificity of mechanical ventilation popular science content; second, encourage and support professional medical personnel to participate in popular science creation, provide targeted creation training and traffic support, and guide them to translate professional medical knowledge into easy-to-understand content suitable for public acceptance; third, develop differentiated popular science content tailored to the user characteristics and dissemination rules of each platform, realize the hierarchical and precise dissemination of mechanical ventilation knowledge, and enhance the relevance and effectiveness of science popularization[ 36 ]. Limitations This study has certain limitations that should be acknowledged: First, as a cross-sectional study, data were collected concentratedly in November 2025, and only a single Chinese keyword, “机械通气” (mechanical ventilation), was used for searching. This may have led to the omission of relevant video samples using alternative terms such as “呼吸机使用” (ventilator use) or “呼吸支持技术” (respiratory support technology), and thus failed to fully reflect the overall landscape of mechanical ventilation popular science content on these platforms. Second, the study only included content from three major Chinese short-video platforms, excluding foreign language platforms and other Chinese platforms such as Xiaohongshu and Kwai, thus limiting the external validity of the research results. Third, there are significant differences in the number of included videos among the three platforms, with Bilibili accounting for 59.9%, which may result in the research results being influenced by the characteristics of this platform and introducing a certain degree of selection bias. Fourth, although the two reviewers received unified training and achieved good consistency in scoring, the evaluation of video quality and completeness still involves a certain degree of subjective judgment, which may have a slight impact on the research results. Conclusion This cross-sectional study systematically analyzed mechanical ventilation popular science videos on three major Chinese short-video platforms (Bilibili, TikTok, and WeChat Channels) and found that such videos have moderate overall quality, and both content reliability and completeness require further improvement. Each platform has distinct characteristics: Bilibili offers the most complete content but low user interaction; TikTok has a higher proportion of professionally certified personnel and strong user interaction but insufficient content completeness; WeChat Channels demonstrates acceptable reliability but poor content completeness. Videos released by professionally certified personnel are superior to those by non-certified ones in terms of content reliability and the completeness of core technical dimensions. Video duration and the number of likes is weakly positively correlated with video production quality and content reliability, respectively, while overall user interaction data cannot reflect the true professional level and scientificity of the content. Short videos have become an important and primary channel for the public to obtain health knowledge, and the effective popularization of critical care medicine knowledge such as mechanical ventilation is crucial for improving national health literacy and public health cognition. In the future, health administrative departments, short-video platforms, and medical institutions need to collaborate to strengthen the professional review of medical popular science content, optimize the creator certification and incentive mechanism, encourage and guide professional medical staff to create accurate and accessible popular science content, and develop differentiated content based on the characteristics of each platform to enhance the accuracy, completeness, and comprehensibility of mechanical ventilation popular science. It is recommended that the public prioritize content released by professionally certified medical creators when accessing such highly professional medical knowledge on short-video platforms, exercise caution and critical thinking in information discrimination, and avoid being misled by false or fragmented information. Declarations Acknowledgments The authors would like to thank the participants for their assistance in this study. Funding The authors received no financial support for the research, authorship, and/or publication of this article. Authors’ Contributions Conceptualization: Q Fan (lead), L Huang (equal) Data curation: Q Fan(lead), L Huang (equal), X Jing (supporting) Formal analysis: Q Fan (lead), L Huang (equal), X Jing (supporting) Methodology: Q Fan (lead), L Huang (equal) Visualization: Q Fan (lead), L Huang (equal), L Tang (supporting) Writing – original draft: Q Fan (lead), L Huang (equal) Writing – review & editing: Q Fan (lead), L Huang (equal), X Jing (supporting), C Qiu (supporting), Z Gu (supporting), Z Hang (supporting), K Wei (supporting) All authors have read and agreed to the published version of the manuscript. Conflict of Interest All authors declare no conflicts of interest relevant to this article. Data availability Statement All data generated or analyzed during this study are included in this published article and its supplementary information files. References Pham T, Brochard LJ, Slutsky AS. Mechanical Ventilation: State of the Art. Mayo Clin Proc. 2017 Sep;92(9):1382–400. 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PMID: 41509869. doi: 10.1177/20552076251412693. Li Q, Jin L, Shi K, Zheng X. Short-video platforms as sources of atherosclerosis information: A cross-sectional content analysis. Medicine (Baltimore). 2025 Oct 3;104(40):e45006. PMID: 41054099. doi: 10.1097/md.0000000000045006. Wang Q, Ma S, Ma D, Wang C, Qi X, Liu S, et al. Video in Chinese short video sharing platforms as a source of information on sleep disorders: A cross-sectional content analysis study. Digit Health. 2026 Jan–Dec;12:20552076261415944. PMID: 41602943. doi: 10.1177/20552076261415944. Xiao L, Min H, Wu Y, Zhang J, Ning Y, Long L, et al. Public's preferences for health science popularization short videos in China: a discrete choice experiment. Front Public Health. 2023;11:1160629. PMID: 37601206. doi: 10.3389/fpubh.2023.1160629. Wang Z, Hu C, Zhou B, Wan M. Quality evaluation of stroke-related information on TikTok: a cross-sectional study. Sci Rep. 2025 Dec 12;16(1):1843. PMID: 41388070. doi: 10.1038/s41598-025-31464-6. Jin Z, Zun C, Liang S, Sida L, Dong L, Fei X, et al. The reliability and quality of short videos as health information of guidance for bowel sounds: a cross-sectional study. Front Public Health. 2025;13:1696018. PMID: 41341453. doi: 10.3389/fpubh.2025.1696018. Gong X, Chen M, Ning L, Zeng L, Dong B. The Quality of Short Videos as a Source of Coronary Heart Disease Information on TikTok: Cross-Sectional Study. JMIR Form Res. 2024 Sep 3;8:e51513. PMID: 39226540. doi: 10.2196/51513. Song H, Omori K, Kim J, Tenzek KE, Morey Hawkins J, Lin WY, et al. Trusting Social Media as a Source of Health Information: Online Surveys Comparing the United States, Korea, and Hong Kong. J Med Internet Res. 2016 Mar 14;18(3):e25. PMID: 26976273. doi: 10.2196/jmir.4193. Additional Declarations No competing interests reported. Supplementary Files Appendix1assessmentscale.doc Appendix2data.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviewers invited by journal 28 Apr, 2026 Editor invited by journal 06 Apr, 2026 Editor assigned by journal 03 Apr, 2026 Submission checks completed at journal 03 Apr, 2026 First submitted to journal 25 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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2","display":"","copyAsset":false,"role":"figure","size":123718,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of video completeness scores among different platforms\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9224693/v1/eb3bafc363fdf0d24ebfc343.png"},{"id":108587846,"identity":"bb622421-032d-4169-8949-6c21b3a57664","added_by":"auto","created_at":"2026-05-06 09:12:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":151796,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of videos by publisher certification status\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9224693/v1/a0c665323e43f09617b16c53.png"},{"id":108587938,"identity":"d2d9114d-f3de-4539-92a6-5b08b9547fad","added_by":"auto","created_at":"2026-05-06 09:12:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":718757,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9224693/v1/9a1945b4-3d8d-4f64-b296-bf4c6ed7940b.pdf"},{"id":108587829,"identity":"db946d78-2c7c-45a0-82d7-df50f63d0185","added_by":"auto","created_at":"2026-05-06 09:12:04","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":110592,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1assessmentscale.doc","url":"https://assets-eu.researchsquare.com/files/rs-9224693/v1/ee969b61adec480acd231aaa.doc"},{"id":108587755,"identity":"4bc0c760-4dad-42f8-b7bc-48a5be779c45","added_by":"auto","created_at":"2026-05-06 09:12:00","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32780,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix2data.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9224693/v1/964d2be594e19e25f754ba29.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quality and Reliability Evaluation of Mechanical Ventilation-Related Video Content on Short-Video Platforms: A Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMechanical ventilation is a core life-support technology in intensive care units, providing essential respiratory support for critically ill patients such as those with respiratory failure. It plays an irreplaceable role in clinical scenarios including critical illness management and perioperative respiratory care [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Since the global spread of the COVID-19 pandemic, the clinical application of respiratory support measures (e.g., oxygen therapy and mechanical ventilation) has garnered widespread public attention, and the demand for accessible and comprehensible medical knowledge related to mechanical ventilation has increased significantly [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. With the rapid advancement of the Internet and new media technologies, social media and online video platforms have become vital carriers for medical knowledge dissemination. Among them, short-video platforms represented by Bilibili, TikTok, and WeChat Channels have emerged as important and primary channels for the public to obtain health and medical information, as well as key venues for health science popularization, owing to their characteristics of rapid dissemination, intuitive presentation, and broad user coverage [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSocial media is increasingly utilized by patients to self-educate on disease processes and to identify hospitals, physicians, and physician networks with optimal expertise for their conditions. Patients are no longer passive consumers of health information; instead, they play an active role in the delivery of health services through online environments. However, the regulation of health information sources on such platforms remains a major challenge, leading to inconsistent information quality overall. Misleading or inaccurate health information can not only be detrimental to patients but also erode public trust in physicians and undermine doctor-patient relationships [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn China, the \u003cem\u003eHealthy China 2030 Planning Outline\u003c/em\u003e explicitly prioritizes \"strengthening health education to improve national health literacy\" and emphasizes \"utilizing internet information technologies to conduct targeted health science popularization\"[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which provides a strategic direction for new media platforms to develop high-quality health science content [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Despite this guidance, short-video platforms still face notable shortcomings in its implementation. Low entry thresholds, the absence of standardized professional review mechanisms, and inadequate content quality control have resulted in highly variable quality of health science content on these platforms [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], with unvalidated medical information and fragmented professional knowledge spreading widely [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. As a highly specialized critical medical technology that integrates multidisciplinary knowledge and has strict clinical operational requirements, the accuracy, completeness, and reliability of mechanical ventilation popular science content directly shape public cognition. Biased or incomplete content not only fails to achieve the goals of science popularization but may also mislead public understanding of clinical medical practices, thereby disrupt doctor-patient communication and even influence clinical decision-making [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. To scientifically evaluate the quality of online health information, researchers commonly adopt the Global Quality Scale (GQS) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]to assess the overall scientificity and production quality of videos, and the well-validated modified DISCERN (mDISCERN) tool [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] to evaluate the reliability of video such content. In recent years, these two tools have been widely applied in the quality evaluation of short videos focused on various medical topics, including sleep disorders [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], liver cancer [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], chronic renal failure [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and uraemia [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNevertheless, there is currently a lack of systematic evaluation on the quality and reliability of mechanical ventilation popular science content\u0026mdash;a core technology in critical care medicine\u0026mdash;on Chinese short-video platforms. Therefore, this study adopted a cross-sectional research design to analyze mechanical ventilation-related videos on three major Chinese short-video platforms (Bilibili, TikTok, and WeChat Channels). We systematically assessed the content quality, reliability, and completeness of these videos, clarified the current status and existing problems of mechanical ventilation science popularization via short videos, and aimed to provide empirical evidence for regulating the dissemination of medical popular science content on short-video platforms and optimizing the health science popularization ecosystem, thereby facilitating the implementation of targeted health science popularization practices.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSearch Strategy and Data Collection\u003c/h2\u003e \u003cp\u003eThis study adopted a cross-sectional research design. From November 10 to November 11, 2025, relevant video samples were collected by searching three major mainstream Chinese short-video platforms (Bilibili, TikTok, and WeChat Channels). The search was performed using the single Chinese keyword \u003cb\u003e\u0026ldquo;机械通气\u0026rdquo; (mechanical ventilation)\u003c/b\u003e. To minimize bias caused by personalized recommendation algorithms, a new account was created and logged into on each short-video platform, and searches were conducted on both the web and mobile app interfaces. The search was not restricted by video release date, and all publicly available videos matching the keyword were included in the initial screening. Prior to data collection, inclusion and exclusion criteria were defined. Inclusion criteria: (1) The video language is Chinese; (2) The popular science content focuses on mechanical ventilation. Exclusion criteria: (1) Duplicate videos; (2) Videos unrelated to \"mechanical ventilation\" or merely news reports; (3) Silent videos; (4) Non-Chinese videos. To ensure the consistent application of these criteria, the following steps were implemented: duplicate content was identified by comparing video titles and core information; relevance was independently evaluated by two researchers based on thematic focus; and advertising materials were excluded based on commercial intent. All exclusions were reviewed, and any discrepancies were resolved by a third researcher to reach a consensus. The following characteristics of the retrieved videos were extracted and analyzed: video duration, number of likes, number of comments, number of favorites, video platform, release date, publisher's certification status, and video content theme. Video data were extracted within 24 hours after searching to minimize changes in engagement indicators over time. All data were recorded using Microsoft Excel.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVideo Evaluation\u003c/h3\u003e\n\u003cp\u003eVideo quality was independently assessed using two validated instruments: the GQS and the modified DISCERN tool. Two registered nurses with more than 5 years of work experience in intensive care units served as reviewers. Both possessed practical experience in the clinical application of mechanical ventilation, received unified training on the scales, and proficiently mastered the scoring criteria of GQS and modified DISCERN. The two reviewers independently performed information extraction and quality scoring of the videos; for videos with controversial scores, a respiratory therapist acted as a third researcher for review, and the final score was determined through consultation among the three researchers. GQS is a widely used video scoring tool for evaluating the quality of health information presented in videos. It is a 5-point scale, with a score of 1 indicating poor quality and limited utility, and a score of 5 indicating excellent quality, clarity, comprehensiveness, and informativeness. The modified DISCERN tool is used to assess the reliability of videos, encompassing five domains: clarity, reliability, content balance, other information sources, and future perspectives. Scores range from 0 to 5 points (6 grades in total) based on \"yes\" or \"no\" responses, with higher scores indicating stronger content reliability. Additionally, a self-designed completeness scoring system was developed: combining the professional knowledge system of mechanical ventilation, content completeness was evaluated from four core dimensions\u0026mdash;concepts and principles, indications, modes and core parameters with waveform interpretation, and prevention and complications. Each dimension was scored as \"not mentioned (0 points)\", \"partially explained (1 point)\", or \"fully explained (2 points)\", with a total score ranging from 0 to 8 points. Higher scores indicate better content completeness.\u003c/p\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eThis study does not require ethical approval as it only analyzes publicly accessible short videos and does not involve clinical data, human specimens, animal experiments, or personal privacy information.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eIBM SPSS 31.0 and R 4.5.1 software were used for statistical analysis. First, a normality test was performed on all data, and the results indicated that all data in this study followed a non-parametric distribution. Therefore, continuous variables were expressed as median (interquartile range) [M (IQR)], and categorical variables were presented as frequency (percentage) [n (%)]. The chi-square test was used for comparing categorical variables among multiple groups, and Fisher's exact test was employed when the theoretical frequency was \u0026lt;\u0026thinsp;5. The Mann-Whitney U test was used for comparing continuous variables between two groups, and the Kruskal-Wallis H test was applied for comparing continuous variables among three or more groups. Spearman's rank correlation analysis was conducted to explore correlations among various video variables, and Cohen's kappa coefficient was used to assess the consistency of scores between the two reviewers. A kappa value\u0026thinsp;\u0026gt;\u0026thinsp;0.75 indicates good consistency. A p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVideo Screening\u003c/h2\u003e \u003cp\u003eA total of 320 videos were collected as initial screening samples through keyword search on the three short-video platforms (Bilibili, TikTok, and WeChat Channels), including 221 on Bilibili, 65 on TikTok, and 34 on WeChat Channels. According to the inclusion and exclusion criteria, 65 duplicate videos, 74 irrelevant videos, 9 silent videos, and 15 non-Chinese videos were excluded sequentially. Finally, 157 videos were included for subsequent analysis, with 94 on Bilibili (59.9%), 32 on TikTok (20.4%), and 31 on WeChat Channels (19.7%). The video inclusion process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBasic Characteristics of Videos\u003c/h3\u003e\n\u003cp\u003eAmong the 157 included videos, 23 (14.6%) were released by professionally certified personnel, and 134 (85.4%) were released by non-professionally certified individuals. The median video duration was 1139 seconds (IQR: 247\u0026ndash;2262), and there was a statistically significant difference in video duration among the platforms. User interaction indicators showed a highly skewed distribution, with a median of 27 likes (IQR: 9\u0026ndash;72), 1 comment (IQR: 0\u0026ndash;4), and 75 favorites (IQR: 21\u0026ndash;225). The results of video quality and completeness scoring revealed a median completeness score of 2 points (IQR: 1\u0026ndash;3), a median GQS score of 3 points (IQR: 3\u0026ndash;3), and a median mDISCERN score of 3 points (IQR: 2\u0026ndash;3). The consistency test of scores between the two reviewers showed that the Cohen's kappa values for GQS, mDISCERN, and completeness scores were 0.80, 0.82, and 0.81, respectively, indicating good consistency between the two reviewers' ratings. The basic characteristics of the videos are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic characteristics of videos\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;157\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort-video platform, [n (%)]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilibili\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94(59.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTikTok\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(20.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeChat Channels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31(19.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVideo source, [n (%)]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessionally certified personnel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(14.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-professionally uncertified personnel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134(85.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVideo duration [seconds, median (IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1139(247\u0026ndash;2262)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of likes [median (IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(9\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of comments [median (IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of favorites [median (IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75(21\u0026ndash;225)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompleteness score [median (IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGQS score [median (IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(3\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emDISCERN score [median (IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(2\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eShort-Video Content Analysis\u003c/h3\u003e\n\u003cp\u003eAnalysis of the completeness of the four core dimensions of the included videos showed that the overall completeness of mechanical ventilation-related popular science content was poor, with only 3.2% of the videos able to fully elaborate on all four core dimensions. Among them, 84.1% of the videos only partially mentioned or did not mention concepts/principles, 66.2% did not reference indications-related content, 78.3% failed to cover the prevention and complications dimension, and only the mode/parameters/waveform dimension had a relatively high full explanation rate of 38.2% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA comparison of content completeness among platforms revealed that the video content completeness on Bilibili was significantly superior to that on TikTok and WeChat Channels. The median score for the mode/parameters/waveform dimension was 2 (IQR:1\u0026ndash;2), which was significantly higher than that on TikTok[1 (IQR:0\u0026ndash;2)] and WeChat Channels [1 (IQR:0\u0026ndash;1)] (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27.072, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); the median total completeness score was 3 (IQR:2\u0026ndash;3), which was higher than that on TikTok[2 (IQR:2\u0026ndash;3)] and WeChat Channels [1 (IQR:1\u0026ndash;3)] (\u003cem\u003eH\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.495, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024); no statistically significant differences were observed in the concepts/principles, indications, and prevention and complications dimensions among the three platforms (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) ((see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of video content completeness\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVideo content\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot mentioned (0 points)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePartially explained (1 point)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFully explained (2 points)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcepts/principles, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71 (45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (15.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndications, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102 (66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (7.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModes/parameters/waveforms, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60 (38.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevention and complications, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123 (78.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of video content completeness scores among different short-video sharing platforms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContent dimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBilibili (n\u0026thinsp;=\u0026thinsp;94) Median [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTikTok(n\u0026thinsp;=\u0026thinsp;32) Median [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeChat Channels (n\u0026thinsp;=\u0026thinsp;31) Median [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eH\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcepts/principles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [0\u0026ndash;1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 [0\u0026ndash;1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [0\u0026ndash;1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 [0\u0026ndash;1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 [0\u0026ndash;1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 [0\u0026ndash;1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModes/parameters/waveforms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 [0\u0026ndash;2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [0\u0026ndash;1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevention and complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 [0\u0026ndash;1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal completeness score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eFootnote: Kruskal-Wallis H test was used; * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eVideo Quality and Reliability Evaluation\u003c/h2\u003e \u003cp\u003eThe overall quality and reliability of the included videos were at a moderate level, with a median GQS score of 3 points (IQR: 3\u0026ndash;3) and a median mDISCERN score of 3 points (IQR: 2\u0026ndash;3) (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Significant differences in video quality and reliability were observed among the three platforms: the GQS scores of Bilibili and WeChat Channels were similar and significantly higher than that of TikTok (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01); the mDISCERN scores of TikTok and WeChat Channels were not significantly different and both significantly higher than that of Bilibili (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA comparison of user interaction and publisher certification status among platforms showed that Bilibili had the longest video duration [2044 seconds (IQR:1214\u0026ndash;2791)] but the lowest user interaction, with the number of likes [15 (IQR:7\u0026ndash;38)] and comments [1 (IQR:0\u0026ndash;3)] significantly lower than those of TikTok(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01); TikTok had the strongest user interaction, with a median of 91 likes (IQR:25\u0026ndash;1574) and 5 comments (IQR:0\u0026ndash;40), both the highest among the three platforms (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01); no statistically significant difference in the number of favorites was found among the three platforms (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eChi-square test results indicated a significant association between video platform and publisher's certification status (χ\u0026sup2;=38.914, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Bilibili had the highest proportion of uncertified publishers (97.9%), TikTok had the highest proportion of professionally certified publishers (46.9%), and WeChat Channels had 80.6% of uncertified publishers (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A comparison of video quality between professionally certified and non-certified publishers showed that the mDISCERN score of certified publishers [3 (IQR:2\u0026ndash;3)] was significantly higher than that of non-certified publishers [3 (IQR:3\u0026ndash;4)] (\u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2246.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and the completeness score of the mode/parameters/waveform dimension [1 (IQR:1\u0026ndash;2)] was also significantly higher than that of non-certified publishers [1 (IQR:0\u0026ndash;2)] (\u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1172.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047); no statistically significant differences were observed in GQS score, total completeness score, and other dimension scores between the two groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison among different short-video sharing platforms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBilibili (n\u0026thinsp;=\u0026thinsp;94) Median [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTikTok(n\u0026thinsp;=\u0026thinsp;32) Median [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeChat Channels (n\u0026thinsp;=\u0026thinsp;31) Median [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVideo duration (seconds)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2044 [1214\u0026ndash;2791]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197 [96\u0026ndash;343]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e215 [137\u0026ndash;440]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of likes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 [7\u0026ndash;38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 [25\u0026ndash;1574]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 [12\u0026ndash;67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of comments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [0\u0026ndash;3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 [0\u0026ndash;40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 [0\u0026ndash;2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of favorites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85 [31\u0026ndash;228]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 [15\u0026ndash;1206]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 [13\u0026ndash;169]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGQS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emDISCERN score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal completeness score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVideo source, [n (%)]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUncertified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (97.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (80.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCertified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (46.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eFootnote: All continuous variables were non-normally distributed and expressed as median [interquartile range]. *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of videos between certified and non-certified accounts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-certified (n\u0026thinsp;=\u0026thinsp;134) Median [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCertified (n\u0026thinsp;=\u0026thinsp;23) Median [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eU\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGQS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1606.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emDISCERN score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2246.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal completeness score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1498.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcepts/principles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [0\u0026ndash;1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 [0\u0026ndash;1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1553.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [0\u0026ndash;1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 [0\u0026ndash;1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1834.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModes/parameters/waveforms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 [0\u0026ndash;2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1172.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevention and complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 [0\u0026ndash;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1530.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eFootnote: Mann-Whitney U test was used; *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analysis\u003c/h2\u003e \u003cp\u003eSpearman's rank correlation analysis results demonstrated a significant positive correlation between video GQS score and mDISCERN score (r\u0026thinsp;=\u0026thinsp;0.391, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); a significant positive correlation between video duration and GQS score (r\u0026thinsp;=\u0026thinsp;0.198, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), but no significant correlation with mDISCERN score (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05); the number of likes was significantly positively correlated with mDISCERN score (r\u0026thinsp;=\u0026thinsp;0.200, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012), but no significant correlation with GQS score (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05); no significant correlations were found between the number of comments or favorites and GQS score or mDISCERN score (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between video GQS score, mDISCERN score, and user interaction indicators (Spearman\u0026rsquo;s rank correlation analysis)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGQS Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emDISCERN Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emDISCERN Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVideo Duration (seconds)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Likes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Comments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Favorites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eFootnote: Spearman\u0026rsquo;s rank correlation analysis; *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ***\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically evaluated the quality, reliability, and completeness of mechanical ventilation-related popular science videos on three major Chinese short-video platforms (Bilibili, TikTok, and WeChat Channels). The results indicated that these videos were of moderate overall quality, with suboptimal reliability and a notable lack of content completeness. Significant inter-platform differences in content characteristics were observed, and videos released by professionally certified publishers demonstrated distinct advantages in reliability and partial dimension completeness. These findings are consistent with the results of short-video quality evaluation studies in other medical fields such as amblyopia [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and colorectal polyps [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], reflecting the common challenges of popularizing highly professional medical technologies on short-video platforms.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCurrent Status and Underlying Causes of Video Quality and Reliability\u003c/h2\u003e \u003cp\u003eThe median GQS and mDISCERN scores of the included videos in this study were both 3 points, indicating an overall moderate to low level of quality and reliability, and only 14.6% of the videos were released by professionally certified personnel\u0026mdash;a proportion much lower than the professional participation rate in the popularization of common diseases knowledge [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This situation primarily stems from the low entry thresholds of short-video platforms, which allow non-professional individuals to freely publish medical-related content without strict review. Moreover, the review mechanisms of most short-video platforms lack the participation of professional medical personnel, making it difficult to ensure the accuracy and scientificity of highly professional content such as mechanical ventilation knowledge.\u003c/p\u003e \u003cp\u003eMeanwhile, relevant videos on platforms such as Bilibili are mostly recordings of professional medical lectures. Although such content possesses high professionalism and scientificity, it lacks popularized translation and simplified explanation, making it difficult for the general public to understand and accept. In contrast, content created by non-professional creators is prone to knowledge biases and even errors due to the lack of systematic medical training. These two factors have jointly contributed to the relatively low overall quality and reliability scores of mechanical ventilation popular science videos on short-video platforms [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCore Issues of Video Content Completeness\u003c/h2\u003e \u003cp\u003eThis study found that the completeness of mechanical ventilation popular science videos was generally poor, with only 3.2% of the videos able to fully cover the four core dimensions. The lack of information in the prevention and complications, and indications dimensions was the most severe, which is similar to the findings of studies on lung cancer popular science short videos [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The inherent time constraint of short videos is a key contributing factor: creators struggle to comprehensively and systematically elaborate on interdisciplinary mechanical ventilation knowledge within a limited video duration, and thus often choose relatively intuitive and easy-to-present content such as ventilation modes and core parameters for creation while neglecting core clinical information such as clinical indications and complication prevention [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In addition, non-professional creators lack systematic critical care medicine knowledge and can only compile and share fragmented information obtained from the internet; even popular science content created by professional medical personnel is mostly fragmented due to the pursuit of short and concise expression adapted to short-video platforms, leading to insufficient overall content completeness. Such serious information gaps may not only hinder the public's comprehensive and accurate understanding of mechanical ventilation but also potentially lead to misunderstandings of clinical medical practices and even incorrect health decision-making.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eImpact of Platform Heterogeneity and Publisher Qualifications\u003c/h2\u003e \u003cp\u003eThe significant differences in video quality, user interaction, and the proportion of certified publishers among the three platforms are closely related to the inherent user positioning and dissemination mechanisms of each platform [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Bilibili is positioned as a platform for knowledge sharing and professional content dissemination, thus achieving the optimal video content completeness among the three platforms. However, the relatively academic professional nature of its content leads to low user interaction and an extremely low proportion of professionally certified publishers. TikTok has a huge and diverse user base covering all age groups and education levels, and its mature Blue V professional certification mechanism has attracted more professional creators to join, resulting in the strongest user interaction among the three platforms. Nevertheless, due to the platform's emphasis on content interestingness and short duration, the professionalism and comprehensiveness of mechanical ventilation popular science content are insufficient. WeChat Channels is a short-video platform based on social circle communication, which exhibits obvious fragmented content characteristics due to its strong social attributes, with both content quality and reliability at a moderate level.\u003c/p\u003e \u003cp\u003eFurthermore, this study confirmed that videos by professionally certified publishers are significantly superior to those by non-certified ones in terms of reliability and the completeness of ventilation modes/parameters/waveforms dimension. However, there is no significant difference in overall quality (GQS score) and total content completeness score between the two groups. This indicates that the current identity certification mechanisms of short-video platforms only verify the professional qualifications of creators but do not evaluate or regulate the quality and completeness of their published popular science content. Some content released by certified publishers still remains fragmented and overly academic, failing to meet the popular science needs of the general public [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between Video Variables and User Interaction Indicators\u003c/h2\u003e \u003cp\u003eThis study identified a significant positive correlation between GQS score and mDISCERN score, suggesting that the higher the video quality of mechanical ventilation popular science videos, the relatively stronger the content reliability [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Video duration was positively correlated with GQS score, indicating that longer videos are more likely to achieve detailed content elaboration and better production quality, but have no significant correlation with reliability which may be due to the fact that reliability is more dependent on the professional background of creators rather than video length. The number of likes was only weakly correlated with mDISCERN score, and the number of comments and favorites had no significant correlation with video quality and reliability scores. This result is consistent with relevant studies on premature ovarian failure [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and atherosclerosis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], indicating that user interaction indicators on short-video platforms are more influenced by non-professional factors such as content interestingness and dissemination popularity, and cannot truly reflect the professional quality and scientificity of the content [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Meanwhile, the strong professionalism and low public awareness of mechanical ventilation led to a relatively narrow target audience, resulting in relatively low overall interaction data of such videos [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This also suggests that creators need to balance video duration, professional content quality and user-friendly presentation to improve the dissemination effect and popular science value of mechanical ventilation videos [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePotential and Development Direction of Health Science Popularization on Short-Video Platforms\u003c/h2\u003e \u003cp\u003eDespite the numerous challenges in current mechanical ventilation short-video popularization, the inherent advantages of short-video platforms\u0026mdash;such as rapid dissemination and wide coverage\u0026mdash;make them important carriers for critical care medicine knowledge popularization [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Based on the results of this study, future optimization and improvement of mechanical ventilation health science popularization on short-video platforms should focus on three key aspects: first, strengthen the professional review mechanism of short-video platforms, establish a specialized professional medical review team composed of critical care physicians, respiratory therapists and nursing specialists, and improve the accuracy and scientificity of mechanical ventilation popular science content; second, encourage and support professional medical personnel to participate in popular science creation, provide targeted creation training and traffic support, and guide them to translate professional medical knowledge into easy-to-understand content suitable for public acceptance; third, develop differentiated popular science content tailored to the user characteristics and dissemination rules of each platform, realize the hierarchical and precise dissemination of mechanical ventilation knowledge, and enhance the relevance and effectiveness of science popularization[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has certain limitations that should be acknowledged: First, as a cross-sectional study, data were collected concentratedly in November 2025, and only a single Chinese keyword, \u0026ldquo;机械通气\u0026rdquo; (mechanical ventilation), was used for searching. This may have led to the omission of relevant video samples using alternative terms such as \u0026ldquo;呼吸机使用\u0026rdquo; (ventilator use) or \u0026ldquo;呼吸支持技术\u0026rdquo; (respiratory support technology), and thus failed to fully reflect the overall landscape of mechanical ventilation popular science content on these platforms. Second, the study only included content from three major Chinese short-video platforms, excluding foreign language platforms and other Chinese platforms such as Xiaohongshu and Kwai, thus limiting the external validity of the research results. Third, there are significant differences in the number of included videos among the three platforms, with Bilibili accounting for 59.9%, which may result in the research results being influenced by the characteristics of this platform and introducing a certain degree of selection bias. Fourth, although the two reviewers received unified training and achieved good consistency in scoring, the evaluation of video quality and completeness still involves a certain degree of subjective judgment, which may have a slight impact on the research results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis cross-sectional study systematically analyzed mechanical ventilation popular science videos on three major Chinese short-video platforms (Bilibili, TikTok, and WeChat Channels) and found that such videos have moderate overall quality, and both content reliability and completeness require further improvement. Each platform has distinct characteristics: Bilibili offers the most complete content but low user interaction; TikTok has a higher proportion of professionally certified personnel and strong user interaction but insufficient content completeness; WeChat Channels demonstrates acceptable reliability but poor content completeness. Videos released by professionally certified personnel are superior to those by non-certified ones in terms of content reliability and the completeness of core technical dimensions. Video duration and the number of likes is weakly positively correlated with video production quality and content reliability, respectively, while overall user interaction data cannot reflect the true professional level and scientificity of the content. Short videos have become an important and primary channel for the public to obtain health knowledge, and the effective popularization of critical care medicine knowledge such as mechanical ventilation is crucial for improving national health literacy and public health cognition. In the future, health administrative departments, short-video platforms, and medical institutions need to collaborate to strengthen the professional review of medical popular science content, optimize the creator certification and incentive mechanism, encourage and guide professional medical staff to create accurate and accessible popular science content, and develop differentiated content based on the characteristics of each platform to enhance the accuracy, completeness, and comprehensibility of mechanical ventilation popular science. It is recommended that the public prioritize content released by professionally certified medical creators when accessing such highly professional medical knowledge on short-video platforms, exercise caution and critical thinking in information discrimination, and avoid being misled by false or fragmented information.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the participants for their assistance in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Q Fan (lead), L Huang (equal)\u003c/p\u003e\n\u003cp\u003eData curation: Q Fan(lead), L Huang (equal), X Jing (supporting)\u003c/p\u003e\n\u003cp\u003eFormal analysis: Q Fan (lead), L Huang (equal), X Jing (supporting)\u003c/p\u003e\n\u003cp\u003eMethodology: Q Fan (lead), L Huang (equal)\u003c/p\u003e\n\u003cp\u003eVisualization: Q Fan (lead), L Huang (equal), L Tang (supporting)\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; original draft: Q Fan (lead), L Huang (equal)\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; review \u0026amp; editing: Q Fan (lead), L Huang (equal), X Jing (supporting), C Qiu (supporting), Z Gu (supporting), Z Hang (supporting), K Wei (supporting)\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflicts of interest relevant to this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePham T, Brochard LJ, Slutsky AS. 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PMID: 26976273. doi: 10.2196/jmir.4193.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-digital-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Digital Health](https://bmcdigitalhealth.biomedcentral.com/)","snPcode":"44247","submissionUrl":"https://submission.nature.com/new-submission/44247/3","title":"BMC Digital Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Short video, Mechanical ventilation, Bilibili, TikTok, WeChat Channels","lastPublishedDoi":"10.21203/rs.3.rs-9224693/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9224693/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMechanical ventilation is a core life-support technology in intensive care units. Since the COVID-19 pandemic, the public's demand for science popularization of such medical knowledge has risen significantly. Short-video platforms have become important carriers for health science popularization, yet the quality of relevant health popularization content varies greatly, and there remains a lack of systematic evaluation on the quality and reliability of mechanical ventilation-related videos.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to systematically evaluate the quality, reliability and completeness of mechanical ventilation-related videos on three major short-video platforms: Bilibili, TikTok and WeChat Channels.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted in November 2025 to analyze 157 mechanical ventilation-related videos from the three major Chinese short-video platforms. The Global Quality Scale (GQS), modified DISCERN (mDISCERN) and a self-designed completeness scale were used to assess the quality, reliability and completeness of the videos. Meanwhile, user interaction indicators including video duration, number of likes, comments and favorites were collected, and statistical analyses were performed to explore the correlations among various video variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 157 mechanical ventilation-related videos were included. The median scores of GQS, mDISCERN and completeness were 3 (interquartile range, IQR: 3\u0026ndash;3), 3 (IQR: 2\u0026ndash;3) and 2 (IQR: 1\u0026ndash;3) respectively, with only 3.2% of the videos containing complete content. Significant inter-platform differences were observed: Bilibili had the optimal content completeness (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and its GQS score was comparable to that of WeChat Channels and significantly higher than that of TikTok(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01); TikTok showed the highest mDISCERN score, user interaction level and proportion of certified publishers (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The scores of videos released by professionally certified publishers were significantly higher than those by non-certified ones (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Positive correlations were found between GQS and mDISCERN scores, between video duration and GQS score, and between the number of likes and mDISCERN score (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eMechanical ventilation science popularization videos on the three short-video platforms were of moderate overall quality with insufficient content completeness, and significant differences existed across the platforms: Bilibili had better content completeness, TikTok featured higher user interaction and a larger proportion of certified publishers, and WeChat Channels had acceptable reliability but poor content completeness. Videos released by professionally certified publishers were of higher quality. It is necessary to strengthen platform regulation and professional review to improve the quality of medical science popularization content.\u003c/p\u003e","manuscriptTitle":"Quality and Reliability Evaluation of Mechanical Ventilation-Related Video Content on Short-Video Platforms: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 09:10:46","doi":"10.21203/rs.3.rs-9224693/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-16T01:57:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131092291947339401497266915592865614781","date":"2026-05-05T11:13:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315132934615558364584788135678549041250","date":"2026-05-03T22:09:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-28T11:13:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-06T06:24:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-03T16:42:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-03T16:41:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Digital Health","date":"2026-03-25T14:46:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-digital-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Digital Health](https://bmcdigitalhealth.biomedcentral.com/)","snPcode":"44247","submissionUrl":"https://submission.nature.com/new-submission/44247/3","title":"BMC Digital Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a4d0c77-f8c6-4d58-9dd5-3c14cf8f328a","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-16T01:57:56+00:00","index":68,"fulltext":""},{"type":"reviewerAgreed","content":"131092291947339401497266915592865614781","date":"2026-05-05T11:13:45+00:00","index":62,"fulltext":""},{"type":"reviewerAgreed","content":"315132934615558364584788135678549041250","date":"2026-05-03T22:09:29+00:00","index":45,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T09:10:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 09:10:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9224693","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9224693","identity":"rs-9224693","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}