A systematic review of methods for studying consumer health YouTube videos, with implications for systematic reviews
Bibliographic record
Abstract
Background. YouTube is an increasingly important medium for consumer health information - with content provided by healthcare professionals, government and non-government organizations, industry, and consumers themselves. It is a rapidly developing area of study for healthcare researchers. We examine the methods used in reviews of YouTube consumer health videos to identify trends and best practices. Methods and Materials. Published reviews of consumer-oriented health-related YouTube videos were identified through PubMed. Data extracted from these studies included type of journal, topic, characteristics of the search, methods of review including number of reviewers and method to achieve consensus between reviewers, inclusion and exclusion criteria, characteristics of the videos reported, ethical oversight, and follow-up. Results. Thirty-three studies were identified. Most were recent and published in specialty journals. Typically, these included more than 100 videos, and were examined by multiple reviewers. Most studies described characteristics of the videos, number of views, and sometime characteristics of the viewers. Accuracy of portrayal of the health issue under consideration was a common focus. Conclusion. Optimal transparency and reproducibility of studies of YouTube health-related videos can be achieved by following guidance designed for systematic review reporting, with attention to several elements specific to the video medium. Particularly when seeking to replicate consumer viewing behavior, investigators should consider the method used to select search terms, and use a snowballing rather than a sequential screening approach. Discontinuation protocols for online screening of relevance ranked search results is an area identified for further development.
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.
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 | 0.242 | 0.589 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.056 | 0.045 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".