Risk models of dating aggression across different adolescent relationships: A developmental psychopathology approach.
Bibliographic record
Abstract
The present study examined physical dating aggression in different adolescent relationships and assessed linear, threshold, and moderator risk models for recurrent aggressive relationships. The 621 participants (59% girls, 41% boys) were drawn from a 1-year longitudinal survey of Canadian high school youths ranging from Grade 9 through Grade 12. Approximately 13% of participants reported recurrent dating aggression across 2 different relationships. Using peer and dyadic risk factors from Time 1 of the study, the authors confirmed a linear risk model, such that adolescents in 2 different violent relationships had significantly more contextual risk factors than did adolescents in 1 or no violent relationship. Further, structural equation modeling assessing moderation of contextual risk factors indicated that, for adolescents with high acceptance of dating aggression, peer aggression and delinquency significantly predicted recurrent aggression in a new relationship. In comparison, for adolescents with low acceptance of dating aggression, negative relationship characteristics significantly predicted recurrent aggression. Acceptance did not moderate concurrent associations between risk factors and aggression in 1 relationship. Results support a developmental psychopathological approach to the understanding of recurrent aggression and its associated risk factors.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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".