Childhood abuse and adult cyberbullying victimization in Canada: The role of community belonging
Bibliographic record
Abstract
Childhood abuse is linked to a higher risk of experiencing cyberbullying victimization in adolescence and young adulthood, while a sense of belonging is associated with a reduced risk and buffers against the mental health effects of childhood trauma. However, there is a lack of Canadian research, particularly studies involving adult samples, examining the connection between childhood abuse and cyberbullying victimization in adulthood. The current study examined whether community belonging is a protective factor in the association between childhood abuse and cyberbullying victimization among adults. Data came from Cycle 34 of the Canadian General Social Survey (n ≈ 14,000). Respondents were asked about the frequency in which they experienced seven childhood abuse variables and about the strength of their current community belonging. A binary logistic regression with bootstrap and person-level weights was conducted, while controlling for demographic variables. Results showed that: 1) for each childhood abuse variable, never experiencing that behaviour was associated with trivial-to-small effects for lower cyberbullying victimization in adulthood compared to those who experienced high levels of that behaviour; 2) as community belonging increased, the odds of adult cyberbullying victimization decreased; and 3) there were no significant moderating effects of community belonging; however, a pattern emerged suggesting that differences in cyberbullying victimization between those who did not experience a given abusive behaviour in childhood and those who did experience high levels of that behaviour were larger when community belonging was weaker and smaller when belonging was stronger. The current study highlights the importance of fostering community connections, for example, through programs, support groups, and volunteering, as a potential way of reducing the risk of cyberbullying among adults who have experienced childhood abuse.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".