Alzheimer disease health-related information on YouTube: A video reviewing study
Bibliographic record
Abstract
Background: Alzheimer disease is a common cause of cognitive impairment among elders. Until now, there is no absolute management to treat the disease. Raising awareness of population regarding Alzheimer disease and its management is a top priority goal for different health organizations. Methods: Recently, using different mass media, specifically YouTube to share Alzheimer health-related information become increasingly apparent. The purpose of this study was to review the content of YouTube videos regarding Alzheimer disease health-related information. The searching key terms of Alzheimer disease, and education, management, and care were used. Videos were critically analyzed for content, method of presentation, and caregivers. Results: A total of 1,050 videos were reviewed, 513 videos were excluded because of misleading and duplication. Five hundred and thirty seven videos were analyzed. The most common type of videos classification was education. The majority of video content reviewed was signs and symptoms of Alzheimer disease. Slide presentation was the main method of presenting information (28.7%). The highest number caregivers in the videos review was for physician (32.4%). The total views of the videos were 4,831,853 views, 32,733 likes, and 1,060 dislikes. Conclusion: YouTube videos of Alzheimer disease were frequently viewed. However, inaccurate or incomplete health-related information was noticeable. Community of caregivers has a clear opportunity to enhance the value and quality of educational material on YouTube. Moreover, the nurses should have a significant role in uploading accurate health-related information regarding Alzheimer disease YouTube 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".