Diversity of Profiles and Coping Among Adolescent Girl Victims of Sexual Dating Violence
Bibliographic record
Abstract
Sexual dating violence (DV) is common among female adolescents, and victims may experience other forms of DV (physical, psychological, and cyberviolence) and report a history of child sexual abuse (CSA). Heterogeneity of these victimization experiences could influence how adolescent girls’ cope. We aimed to identify distinct profiles of victimization experiences among adolescent girls who reported sexual DV and to explore if these profiles were associated to their coping strategies. Of an initial sample of 1,300 female adolescents who completed online questionnaires, 835 (Mage = 16.8 years) reported at least one experience of sexual DV and were included in the analyses. Hierarchical classification with the Two Step analysis revealed four distinct profiles of victimization. A first cluster named Moderate CSA & Cyber-sexual DV (21.4%) is characterized by moderate percentage of all forms of victimization. The second cluster CSA & DV excluding cyber-sexual DV (34.4%) included victims of traditional DV, moderate rates of CSA and no experience of cyber-sexual DV. A third cluster CSA & DV Co-occurrence (20.6%) regrouped victims who have experienced different forms of DV in co-occurrence and CSA. Finally, the fourth cluster, named No CSA & DV Co-occurrence (23.6%) involved victims who have experienced different forms of DV in co-occurrence, but did not report a history of CSA. Analyses revealed significant differences between the profiles on the use of avoidance coping, in their perceived social support, and on help-seeking strategies deployed toward a partner and a health professional. These findings offer cues for prevention and intervention efforts for victimized female adolescents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".