Non-suicidal self-injury, youth, and the Internet: What mental health professionals need to know
Bibliographic record
Abstract
Non-suicidal self-injury (NSSI) content and related e-communication have proliferated on the Internet in recent years. Research indicates that many youth who self-injure go online to connect with others who self-injure and view others' NSSI experiences and share their own through text and videos platforms. Although there are benefits to this behaviour in terms of receiving peer support, these activities can introduce these young people to risks, such as NSSI reinforcement through the sharing of stories and strategies, as well as, risks for triggering of NSSI urges. Due to the nature of these risks mental health professionals need to know about these risks and how to effectively assess adolescents' online activity in order to adequately monitor the effects of the purported benefits and risks associated with NSSI content. This article offers research informed clinical guidelines for the assessment, intervention, and monitoring of online NSSI activities. To help bridge the gap between youth culture and mental health culture, these essentials include descriptions of Community, Social Networking, and Video/Photo Sharing websites and the terms associated with these websites. Assessment of these behaviours can be facilitated by a basic Functional Assessment approach that is further informed using specific recommended online questions tailored to NSSI online and an assessment of the frequency, duration, and time of day of the online activities. Intervention in this area should initially assess readiness for change and use motivational interviewing to encourage substitution of healthier online activities for the activities that may currently foster harm.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".