Turning Points for Perpetrators of Intimate Partner Violence
Bibliographic record
Abstract
Understanding why and how perpetrators of intimate partner violence (IPV) change their behavior is an important goal for both policy development and clinical practice. In this study, the authors investigated the concept of "turning points" for perpetrators of IPV by conducting a systematic review of qualitative studies that investigated the factors, situations, and attitudes that facilitated perpetrators' decisions to change their abusive behavior. Two literature databases were searched and six studies were found that met the inclusion criteria for the systematic review. Most included participants from batterer intervention programs (BIPs). The data indicate that community, group, and individual processes all contribute to perpetrators' turning points and behavioral change. These include identifying key incidents that precede change, taking responsibility for past behavior, learning new skills, and developing relationships within and outside of the BIP. By using a qualitative systematic review, the authors were able to generate a more complete understanding of the catalysts for and process of change in these individuals. Further research, combining quantitative and qualitative approaches, will be helpful in the modification of existing BIPs and the development of new interventions to reduce IPV.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".