Cyber Dating Abuse Victimization: Links With Psychosocial Functioning
Bibliographic record
Abstract
It is well established that technology can be used to enact intimate partner violence (IPV). However, less is known about how cyber dating abuse (CDA) is associated with psychosocial functioning, especially when accounting for other forms of frequently co-occurring IPV victimization. The current study sought to determine the unique associations of CDA victimization when controlling for multiple forms of in-person IPV victimization. Two hundred seventy-eight men and women between 17 and 25 years of age ( M = 20.5, SD = 1.9) who were currently in an intimate relationship for at least 3 months participated in this study. Participants completed questionnaires about their IPV and CDA victimization, as well as a range of indices of psychosocial well-being. Experiencing CDA victimization was related to increased alcohol use for both men and women, and increased fear of partner for women, even after controlling for in-person IPV. For depression, perceived stress, relationship satisfaction, quality of life, social support, and post-traumatic stress, CDA victimization did not predict levels above in-person IPV victimization. Although these results suggest some unique associations between CDA victimization and aspects of psychosocial well-being that require further attention, they also highlight that CDA often occurs within a broader pattern of abuse that includes in-person IPV. These results suggest that the need for prevention and treatment for relationships that involve in-person abuse is still most salient, and that a narrow focus on CDA may limit the utility of prevention and treatment efforts. Further work is needed to integrate research on in-person and CDA victimization, rather than to create a new field of research and practice based solely on CDA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".