A Systematic Review of Social Media Use to Discuss and View Deliberate Self-Harm Acts
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review of studies of social media platforms used by young people to discuss and view deliberate self-harm. STUDY DESIGN: 11 electronic databases were searched from January 2000 to January 2012 for primary research; in June 2014 an updated search of Medline was conducted. Grey literature sources were also searched. Search results were screened by two reviewers. Data were extracted by one reviewer and verified by another. Methodological quality was assessed using the Mixed Methods Appraisal Tool. RESULTS: Due to heterogeneity in study objectives and outcomes, results were not pooled; a narrative analysis is presented. 26 studies were included. Most were conducted in Canada or the UK (30.8% each), used qualitative designs (42.3%), and evaluated discussion forums (73.1%). Participants were most often aged 19-21 years (69.2%), female (mean 68.6%), and 19.2% had a documented history of depression. The social media platforms evaluated were commonly supportive and provided a sense of community among users. Support included suggestions for formal treatment, advice on stopping self-harming behavior, and encouragement. Harms included normalizing and accepting self-harming behavior; discussion of motivation or triggers, concealment, suicidal ideation or plans; and live depictions of self-harm acts. CONCLUSIONS: Although this evidence is limited by its descriptive nature, studies identify beneficial and detrimental effects for young people using social media to discuss and view deliberate self-harm. The connections users make online may be valuable to explore for therapeutic benefit. Prospective, longitudinal investigations are needed to identify short- and long-term potential harms associated with use.
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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 | 0.020 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".