Evaluating Risk and Protective Factors for Suicidality and Self-Harm in Australian Adolescents With Traditional Bullying and Cyberbullying Victimizations
Bibliographic record
Abstract
PURPOSE: To identify and compare important risk and protective factors associated with suicidality and self-harm among traditional bullying and cyberbullying victims aged 14-17-years in Australia. DESIGN: Cross-sectional population-based study. SETTING: Young Minds Matter, a nationwide survey in Australia. SUBJECTS: Adolescents aged 14-17-years (n = 2125). MEASURES: Suicidality and self-harm were outcome variables, and explanatory variables included sociodemographic factors (age, gender, country of birth, household income, location, family type), risk factors (parental distress, family functioning, family history of substance use, child substance use, mental disorder, psychosis, eating disorders, sexual activity) and protective factors (high self-esteem, positive mental health or resilience, school connectedness, sleep) among 2 types of bullying victims-traditional and cyber. Traditional bullying includes physical (hit, kick, push) or verbal (tease, rumors, threat, ignorance), and cyberbullying includes teasing messages/pictures via email, social medial using the internet and/or mobile phones. ANALYSIS: Bivariate analysis and binary logistic regression models. Statistical metrics include Hosmer-Lemeshow Goodness-of-Fit-test, VIF test, Linktest and ROC curve for model performance and fitness. RESULTS: Overall, 25.6% of adolescents were traditional bullying victims and 12% were cyberbullying victims. The percentages of suicidality (34.4% vs 21.6%) and self-harm (32.8% vs 22.3%) were higher in cyberbullying victims than in traditional bullying victims. Girls were more often bullied and likely to experience suicidal and self-harming behavior than boys. Parental distress, mental disorder and psychosis were found to be significantly associated with the increase risk for self-harm and suicidality among both bullying victims (p < 0.05). While, eating disorder and sexual activity increased the risk of suicidality in traditional bullying victims and self-harm in cyberbullying victims, respectively. Positive mental health/resilience and adequate sleep were found be significantly associated with decreased suicidality and self-harm in both bullying victims. CONCLUSION: Suicidality and self-harm were common in bullying victims. The findings highlight that the risk and protective factors associated with suicidality and self-harm among adolescent who experienced traditional and cyberbullying victimization should be considered for the promotion of effective self-harm and suicide prevention and intervention programs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".