Searching for the mechanisms of change: a protocol for a realist review of batterer treatment programmes
Bibliographic record
Abstract
INTRODUCTION: Conflicting results reported by evaluations of typical batterer intervention programmes leave many judicial officials and policymakers uncertain about the best way to respond to domestic violence, and whether to recommend and fund these programmes. Traditional evaluations and systematic reviews tend to focus predominantly on whether the programmes 'worked' (eg, reduced recidivism) often at the exclusion of understanding for whom they may or may not have worked, under what circumstances, and why. METHODS AND ANALYSIS: We are undertaking a realist review of the batterer treatment programme literature with the aim of addressing this gap. Keeping with the goals of realist review, our primary aims are to identify the theory that underlies these programmes, highlight the mechanisms that trigger changes in participant behaviour and finally explain why these programmes help some individuals reduce their use of violence and under what conditions they are effective or not effective. We begin by describing the process of perpetrator treatment, and by proposing an initial theoretical model of behaviour change that will be tested by our review. We then describe the criteria for inclusion of an evaluation into the review, the search strategy we will use to identify the studies, and the plan for data extraction and analysis. ETHICS AND DISSEMINATION: The results of this review will be written up using the RAMESES Guidelines for Realist Synthesis, and disseminated through peer-reviewed publications aimed at the practitioner community as well as presented at community forums, and at violence against women conferences. Ethics approval was not needed.
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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 | 0.286 | 0.341 |
| Meta-epidemiology (narrow) | 0.008 | 0.008 |
| Meta-epidemiology (broad) | 0.019 | 0.017 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.020 | 0.017 |
| Insufficient payload (model declined to judge) | 0.070 | 0.023 |
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".