YouTube Videos as a Source of Information About Clinical Trials: Observational Study
Bibliographic record
Abstract
BACKGROUND: Clinical trials are essential to the advancement of cancer treatment but fewer than 5% of adult cancer patients enroll in a trial. A commonly cited barrier to participation is the lack of understanding about clinical trials. OBJECTIVE: Since the internet is a popular source of health-related information and YouTube is the second most visited website in the world, we examined the content of the top 115 YouTube videos about clinical trials to evaluate clinical trial information available through this medium. METHODS: YouTube videos posted prior to March 2017 were searched using selected keywords. A snowballing technique was used to identify videos wherein sequential screening of the autofill search results for each set of keywords was conducted. Video characteristics (eg, number of views and video length) were recorded. The content was broadly grouped as related to purpose, phases, design, safety and ethics, and participant considerations. Stepwise multivariable logistic regression analysis was conducted to assess associations between video type (cancer vs noncancer) and video characteristics and content. RESULTS: In total, 115 videos were reviewed. Of these, 46/115 (40.0%) were cancer clinical trials videos and 69/115 (60.0%) were noncancer/general clinical trial videos. Most videos were created by health care organizations/cancer centers (34/115, 29.6%), were oriented toward patients (67/115, 58.3%) and the general public (68/115, 59.1%), and were informational (79/115, 68.7%); altruism was a common theme (31/115, 27.0%). Compared with noncancer videos, cancer clinical trials videos more frequently used an affective communication style and mentioned the benefits of participation. Cancer clinical trial videos were also much more likely to raise the issue of costs associated with participation (odds ratio [OR] 5.93, 95% CI 1.15-29.46) and advise patients to communicate with their physician about cancer clinical trials (OR 4.94, 95% CI 1.39-17.56). CONCLUSIONS: Collectively, YouTube clinical trial videos provided information on many aspects of trials; however, individual videos tended to focus on selected topics with varying levels of detail. Cancer clinical trial videos were more emotional in style and positive in tone and provided information on the important topics of cost and communication. Patients are encouraged to verify and supplement YouTube video information in consultations with their health care professionals to obtain a full and accurate picture of cancer clinical trials to make an adequately informed decision about participation.
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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 | 0.008 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".