Quality Assessment of Shock Videos on Video Sharing Platforms: Cross-Sectional Study
Bibliographic record
Abstract
Background: As a highly lethal circulatory failure syndrome, the pathophysiological mechanisms of shock can lead to multiple organ dysfunction syndrome (MODS), which significantly increases the demand for intensive care and the length of hospitalization. There is therefore an urgent need for the public to be informed about health-related issues. In recent years, videos have become a significant medium for health education, and this study aimed to evaluate shock-related videos on video sharing platforms. Objective: The objective of this study is to identify the top 100 videos related to impact on TikTok, Bilibili, and Xiaohongshu. These videos will then be assessed in terms of their effectiveness and credibility. Following this evaluation, relevant recommendations will be provided. Methods: The study included a search for videos related to shock on the three video-sharing platforms: TikTok, Bilibili, and Xiaohongshu. The Global Quality Score (GQS) and mDISCERN tools were used to evaluate the credibility and quality of the videos, in addition to employing the Patient Education Materials Evaluation Tool for Audiovisual Content (PEMAT-A/V). Finally, the video was evaluated by examining disease definitions, clinical manifestations, risk factors, assessment, management, and outcomes. Results: A total of 244 videos (TikTok:87, Bilibili:80, Xiaohongshu:77）were retrieved from the three platforms. The overall video quality was found to be moderately low. The majority of videos were uploaded by health advocates (n=102, 41.8%) and health professionals (n=98, 40.1%). The individual video sources of the GQS were of lower quality (1-3), the mDISCERN scores were moderate (2-4), and the quality of individual users is higher than that of organizational users. The PEMAT A/V scores were as follows: in the overall comprehensibility evaluation, 91% (220) videos of the scores were above 70%; in the actionability evaluation, 65% (157) videos of the scores were below 70%. It should be noted that the actionability scores for different video sources were generally low. In 172 videos (70.4%), the definition of shock and its clinical manifestations were explained in detail, while in 137 videos (56.1%), the definition of shock and its clinical manifestations were also clearly explained. The majority of videos provided a relatively comprehensive explanation of the definition of shock and its clinical signs and symptoms. Conclusions: Our study have demonstrated that the content and information quality of shock videos is unsatisfactory, as a general rule. This underscores the necessity for pertinent regulatory bodies to oversee the caliber of health-related videos, and for content creators to enhance the quality of their content.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".