Dating Violence Experiences among Youths with Same-gender and Multi-gender Dating Partners: A Dyadic Concordance Type Approach
Bibliographic record
Abstract
Dating violence (DV) among youth is widespread and is now established as a significant public health problem. Yet, few studies have assessed DV experiences among youth with same-gender or multi-gender dating partners, and most failed to consider bidirectional DV. We analyzed self-reported dyadic concordance types (DCTs) among 295 youths (52% girls) who dated same-gender and multi-gender partners in the last 12 months using an adapted version of the Conflict in Adolescent Dating Relationships Inventory. Youths were classified in one of three DCTs: self-only (unidirectional perpetration of DV by the participant), partner-only (unidirectional victimization perpetrated by their partner) or both (bidirectional DV, where partners are both perpetrators and victims of DV). Overall prevalence rates of DV among sexual minority youths (SMYs) range from 11.5% for threats, to 51.2% for psychological violence, with physical and sexual violence reported by about one-fourth of participants. The both DCT was the most common pattern for psychological (59.6%) and physical (50.6%) DV across gender, while most threatening behaviors were reported as perpetrated by the partner only (47.1%). Girls were more likely to report sexual DV as partner-only perpetrated (63.6%), whereas boys reported higher rates of both (44.2%) and self-only (34.9%) perpetrated sexual violence. Because healthy intimate relationships can play a supportive and positive role in transitioning toward adulthood, it is crucial that DV prevention becomes more inclusive of sexual and gender diversity.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 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".