Safe not soft: trauma- and violence-informed practice with perpetrators as a means of increasing safety
Bibliographic record
Abstract
Trauma- and violence-informed care (TVIC) has become an important lens to guide health and social services. TVIC emphasizes service providers’ understanding of trauma and its impact, including structural aspects of victimization. Emotionally and physically safe environments, service user opportunities for choice, collaboration and connection, and the use of strengths-based and capacity-building approaches are prioritized. The majority of writing on TVIC has focused on its application to services for survivors of trauma and abuse. In this paper, we argue that a modified trauma-and violence-informed lens has the potential to improve our work with men who perpetrate violence in interpersonal relationships, and even more importantly, that without such a lens, we are likely to miss very important opportunities to act in ways that enhance the safety of potential victims of abuse. Using examples drawn from practice, we explore specific examples of how applying TVIC principles may increase service providers’ ability to recognize and respond to potentially dangerous situations, thereby improving services to perpetrators and enhancing safety for potential victims of abuse.
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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 | 0.034 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.034 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".