Evaluating YouTube as a Source of Education for Patients Undergoing Surgery
Bibliographic record
Abstract
OBJECTIVE: 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. SUMMARY BACKGROUND DATA: YouTube is the largest online video sharing platform and has become a substantial source of health information that patients are likely to access before surgery, yet there has been no systematic assessment of peer-reviewed studies. A comprehensive literature search was conducted using EMBASE, MEDLINE, and Ovid HealthStar from inception through to December of 2021. METHODS: All primary studies evaluating YouTube as a source of patient education relating to surgical procedures (general, cardiac, urology, otolaryngology, plastic, vascular) were included. Study screening and data extraction occurred in duplicate with two reviewers. Characteristics extracted included video length, view count, upload source, overall video educational quality, and quality of individual studies. RESULTS: Among 6,453 citations, 56 studies were identified that examined 6,797 videos with 547 hours of content and 1.39 billion views. There were 49 studies that evaluated the educational quality of the videos. A total of 43 quality assessment tools were used, with each study using a mean of 1.90 assessment tools. Per the global rating for assessments, 34/49 studies (69%) concluded that the overall quality of educational content was poor. CONCLUSIONS: While the impact of non-peer-reviewed YouTube videos on patient knowledge 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".