Systematic review of risk and protective factors for suicidal and self-harm behaviors among children and adolescents involved with cyberbullying
Bibliographic record
Abstract
Cyberbullying is associated with increased risk of suicidal and self-harm behaviors in children and adolescents. However, no review to date has explored factors that exacerbate and mitigate this relationship. This systematic review concerns research on factors that influence the impact of cyberbullying on suicidal and self-harm behaviors. Four bibliographic databases were explored and references in included articles were searched. We identified 727 articles and retained 66 that met inclusion criteria. Research has identified multiple risk factors which have been associated with increased suicide risk in general (mental health problems, substance abuse, loneliness, stress, sexual orientation/gender identity issues and violent behaviors). Others risk factors more specific to cyberbullying were: Autism Spectrum Disorder, Intellectual and Developmental Disorders, obesity, having asthma and severity of cyberbullying. Fewer studies concern protective factors. School connectedness, restrictive style of parenting, parental support, life satisfaction, having a healthy diet, personal skills and having family dinners were associated with less risk of suicidal and self-harm behaviors following cyberbullying. These protective factors suggest prevention strategies to reduce the impacts of cyberbullying by teaching better personal skills, promoting school social connections and proposing family interventions. More research is needed including exploration of the differential impacts of different forms of cyberbullying, and evaluations of the impacts of programs to increase personal skills, improve family relationships and foster school connectedness to reducing suicidal and self-harm behaviors in this vulnerable population.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".