Evaluating YouTube as a Source Of Patient Education for Patients Undergoing Surgery
Bibliographic record
Abstract
Introduction: Online video sharing platforms such as YouTube have become a substantial source of health information that patients are likely to access before surgery. The objective of this systematic review is to characterize the peer-reviewed literature investigating YouTube as a source of patient education for patients undergoing surgery. Methods: A comprehensive literature search was conducted using EMBASE, MEDLINE, and Ovid from inception to December of 2021. Study screening and data extraction occurred in duplicate. All primary studies evaluating YouTube as a source of patient education relating to surgical procedures for the selected specialties (general, cardiac, urology, otolaryngology, and plastic) were included. Descriptive statistics were used to describe data in aggregate. Results: Among 6,453 citations identified, 56 studies were identified that examined 6,524 videos with 1843 hours of content and 1.1 billion views. Among 49 studies that evaluated the educational quality of the videos, 43 quality assessment tools were used, with each study using a mean of 1.88 assessment tools. Per the global rating for assessments, 34/49 studies (69%) concluded that the overall quality of educational content was poor. Conclusion: While the impact of non-peer-reviewed YouTube videos on patient knowledge and preparedness for surgery is unclear, the large amount of online content suggests that they are in demand. The overall educational content of these videos is poor, however, and there is substantial heterogeneity in the quality assessment tools used in their evaluation. A peer-reviewed and standardized approach to online education with video content is needed to better support patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.279 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".