Bibliographic record
Abstract
Introduction: Mental health is a primary determinant of well-being, and as more people look online for mental health information, YouTube is an increasingly important information source. Although authoritative organizations such as the World Health Organization post videos to YouTube, when retrieved these videos are interspersed with personal, commercial, governmental, television or other media segments, and institutional videos. YouTube was searched for videos on mental health to measure user engagement with these videos. It was hypothesized that videos posted to YouTube that contained personal narratives would generate more user engagement in terms of more video view counts, likes, and number of comments. Methods: YouTube was searched for mental health information using three different search terms and phrases: “depression,” “bipolar disorder,” and “mental health.” The first 20 results for the terms depression and bipolar disorder were screen captured and for the search phrase mental health the first 40 videos were screen captured. All 80 videos were categorized according to video producer type and analyzed using YouTube metrics including number of “likes,” view counts, and comments to measure user engagement with the videos. Results: The majority of videos returned in the top results were posted by laypersons and the videos focus on the poster's personal experience (38%) followed by videos produced for television and other media (29%). Videos that contain personal narratives and experiential knowledge generate the most user engagement and are preferred sources for users searching for mental health information. Discussion: Users’ greater engagement with personal videos indicates that there is an important role for librarians and information professionals in assisting users in deciding what mental health information is accurate, authoritative, and reliable regardless of the authority of the video producer. In addition, the results of this research might inform best practices for professional organizations posting videos to YouTube.
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How this classification was reachedexpand
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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".