Helpful or Harmful? An Examination of Viewers' Responses to Nonsuicidal Self-Injury Videos on YouTube
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: To examine viewers' comment responses to nonsuicidal self-injury (NSSI) YouTube videos to determine the potential risks (e.g., NSSI continuation) and benefits (e.g., recovery-oriented social support) of the videos. METHODS: Viewers' comments from the 100 most-viewed NSSI videos on YouTube were examined using two coding rubrics, one for the global nature of comments and one for recovery-oriented themes. Both rubrics were developed using an inductive (bottom-up) approach and had high coding inter-rater reliability (exceeding .80 in all cases). For the global nature of comments, 869 randomly selected comments were evaluated using the rubric, which included 8 coding categories and 22 subcategories. For the examination of recovery-oriented themes, self-disclosure comments (n = 377) were evaluated for nature of recovery statements. RESULTS: Results revealed that the most frequent comments were self-disclosure comments in which individuals shared their own NSSI experiences (38.39%), followed by feedback for the video uploader, including admiration of the video quality (21.95%) or message (17.01%), and admiration for the uploader (15.40%) or encouragement to the video uploader (11.15%). Evaluation of the common self-disclosure comments for recovery-oriented content revealed that the majority did not mention recovery at all (42.89%) and indicated that they were still self-injuring (34.00%). Positive recovery statements were uncommon. CONCLUSIONS: Results suggest that viewers' responses to videos may maintain the behavior (by sharing their own self-injury experiences) and rarely encourage or mention recovery. It is evident that sharing their own experience online is a strong motivator for viewers of NSSI YouTube videos.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it