Domestic and family violence behaviour change programs: An examination of gendered and non-gendered frameworks
Bibliographic record
Abstract
This article sets out to examine the dichotomous frameworks used to inform domestic and family violence (DFV) behaviour change programs (BCPs). Based on a Rapid Evidence Assessment (REA) methodology, we consider what works and what does not work in the delivery of Domestic and Family Violence programs through a gendered and non-gendered framework. This methodology was selected as it supports a balanced assessment of existing published research in the area, allowing for the current knowledge base to be critically examined. As a result, the REA revealed both the strengths and weaknesses of traditional gendered approaches focusing on the Duluth Model and non-gendered therapeutic approaches focusing on Cognitive Behaviour Therapy (CBT). Yet, while strengths and weaknesses can be seen in both the “violence as gendered” and “violence as non-gendered” paradigms, a case is made for only delivering BCPs within a non-gendered framework.
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 | 0.099 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".