A Qualitative Analysis of Methotrexate Self-injection Education Videos on YouTube
Bibliographic record
Abstract
Background Patients are increasingly turning to the Internet for health guidance, requiring awareness from clinicians of constantly changing resources and quality of available information. A previous study demonstrated a minority of YouTube videos were useful for teaching methotrexate (MTX) self-injection; however, YouTube content constantly evolves, and previous results may not represent current videos. This study provides an update on previous work from 2014 evaluating the quality of YouTube videos demonstrating self-administered subcutaneous MTX injections. Our aim was to evaluate how YouTube videos on MTX injection have changed and evaluate the current video quality. Methods “Methotrexate injection” was searched on YouTube. The first 75 videos were analyzed independently by 2 reviewers. Videos were classified as useful, misleading/irrelevant, or a personal patient view and rated for reliability, comprehensiveness, and quality. Results Of the 75 videos reviewed, 12 were classified as useful (16%), 43 misleading/irrelevant (57.3%), and 20 personal patient views (26.7%). Although this represents a substantial increase from previous results in the proportion of videos deemed misleading/irrelevant (57.3% vs. 27.5%) (p = 0.0011), their reliability and global quality scores were higher. Conclusions Concordant with the previous study, only a small proportion of the total videos were deemed useful videos for MTX injection specifically. However, reliability and global quality scores for all videos increased from the previous study, suggesting more videos provide reliable information with regard to MTX overall, even if it does not speak to self-injection directly. Logistics of the YouTube algorithm may still impede access to the “best” videos for patient teaching; therefore, clinicians should be prepared to recommend strategies for patients to find high-quality videos.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".