Medical YouTube Videos and Methods of Evaluation: Literature Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Online medical education has relevance to public health literacy and physician efficacy, yet it requires a certain standard of reliability. While the internet has the potential to be a viable medical education tool, the viewer must be able to discern which information is reliable. OBJECTIVE: Our aim was to perform a literature review to determine and compare the various methods used when analyzing YouTube videos for patient education efficacy, information accuracy, and quality. METHODS: In November 2016, a comprehensive search within PubMed and Embase resulted in 37 included studies. RESULTS: The review revealed that each video evaluation study first established search terms, exclusion criteria, and methods to analyze the videos in a consistent manner. The majority of the evaluators devised a scoring system, but variations were innumerable within each study's methods. CONCLUSIONS: In comparing the 37 studies, we found that overall, common steps were taken to evaluate the content. However, a concrete set of methods did not exist. This is notable since many patients turn to the internet for medical information yet lack the tools to evaluate the advice being given. There was, however, a common aim of discovering what health-related content the public is accessing, and how credible that material is.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 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 it