Association between self-related cognitions and cyberbullying victimization in children and adolescents: A systematic review and meta-analysis
Bibliographic record
Abstract
Self-efficacy, self-esteem, self-concept, and self-blame have been proposed as potential factors in the development and maintenance of cybervictimization in a unidirectional, but also in a cyclic paradigm. Our objective was to synthesize the existing evidence and assess potential moderators of the relationship between these self-related cognitions and cybervictimization. We searched five electronic databases (PsycINFO, PubMed, Scopus, Web of Science and Cochrane) from inception until October 2022. A total of 81 studies were included, encompassing a cohort of 110,095 children and adolescents with a mean age of 11.51 years. Nearly half of the studies were rated as having fair quality. Across the examined self-related cognitions, high level of cybervictimization was associated with low level of self-concept, low self-efficacy and low self-esteem. Cognitions related to self-blame were not statistically significantly associated with cybervictimization in our review. These findings included high heterogeneity and were consistent across sensitivity analyses. Meta-regression analyses revealed that the number of participants significantly moderated the relationship between self-esteem and cybervictimization, but the percentage of victims and mean age of participants did not exhibit significant moderation effects. This preregistered systematic review and meta-analysis showed modest yet statistically significant correlations between self-related cognitions and cybervictimization. The discussion addresses the implications for future research and anti-cyberbullying programs. PROSPERO reference number CRD42021289512. • In 81 studies, 110,095 children and adolescents with a mean age of 11.51 years were analyzed • High levels of cybervictimization was associated with low level of self-concept, self-efficacy and self-esteem • Self-blame was not statistically significant associated with cybervictimization in our review • The number of participants significantly moderated the self-esteem and cybervictimization relationship. • The percentage of victims and mean age of participants did not exhibit significant moderation effects.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".