Romantic attachment and cyber dating violence in adolescence: A dyadic approach
Bibliographic record
Abstract
INTRODUCTION: Little research has documented cyber dating violence (DV)-a type of teen DV with unique characteristics that has been associated with negative consequences. Attachment is central to understanding negative behaviors in the context of relationships and has been associated with other forms of DV in teens. This study used an actor-partner interdependence model (APIM) to examine how cyber-DV victimization and perpetration (direct aggression and control) relate to attachment anxiety and avoidance. METHODS: An online questionnaire was completed by 126 adolescent couples (n = 252; mean age = 17.7) from Quebec, Canada. RESULTS: In almost all couples (96%), at least one partner reported an incident of cyber-control in the previous year, while cyber-aggression was reported in 34% of couples. APIM results revealed that girls' and boys' victimization and perpetration of direct cyber-aggression are associated similarly with both their own high levels of attachment anxiety and their partner's. Concerning cyber-control, results show that boys' and girls' victimization is associated more with their partner's higher level of anxious attachment than their own. Girls' perpetration of cyber-control is associated with both their own high levels of attachment anxiety and their partner's, while for boys' perpetration, their own high levels of anxious attachment were found to play a significantly greater role than their girlfriend's. No significant associations were found for the dimension of avoidant attachment for both cyber-aggression and cyber-control whether perpetration or victimization. CONCLUSION: These findings, which identify potential risk factors for victimization and perpetration of cyber-DV, have implications for research, intervention, and prevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".