The impact of YouTube peer feedback on attitudes toward recovery from non-suicidal self-injury: An experimental pilot study
Bibliographic record
Abstract
BACKGROUND: Non-suicidal self-injury (NSSI) is a serious public health concern facing adolescents and young adults worldwide. Despite growing concern that accessing NSSI content on the internet may negatively influence perceptions toward NSSI recovery, no studies have examined actual impacts. OBJECTIVES: This experimental pilot study assessed the impact of exposure to hopeless versus hopeful peer messages on perceptions toward NSSI recovery. It was hypothesized that exposure to hopeless messages would lead to more negative perceptions about NSSI recovery whereas the opposite would occur for hopeful messages. METHODS: We developed fictional peer comments embedded in a screenshot of an NSSI-themed YouTube video and randomly assigned participants to either hopeless or hopeful recovery-oriented comments. Participants' attitudes toward NSSI recovery, recovery-oriented subjective norms, and recovery self-efficacy were measured pre- and post-exposure using an online questionnaire. RESULTS: Sixty-one participants with a self-reported NSSI history (mean age 20.89 years) completed the online survey. There was a statistically significant effect for attitudes toward recovery. Within the hopeful comment condition, there was an increase in positive attitudes toward recovery and in recovery-oriented subjective norms. Participants exposed to hopeless peer messages did not report an increase in hopeless attitudes toward NSSI recovery. CONCLUSIONS: Our pilot study indicated that exposure to hopeful online messages improved positive attitudes toward recovery and recovery-oriented subjective norms, while exposure to hopeless messages did not increase hopeless attitudes. Future research on the impacts of online peer comments on one's attitude toward NSSI recovery and support-seeking behavior could further inform practices and policies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".