Variables Associated With Attrition From Domestic Violence Treatment Programs Targeting Male Batterers
Bibliographic record
Abstract
Attrition from domestic violence treatment programs is of concern to correctional treatment providers because batterers who do not complete treatment are at higher risk for recidivism. This meta-analysis was conducted to determine the extent to which various demographic, violence-related, and intrapersonal variables predict attrition from domestic violence treatment programs for male batterers. A total of 30 studies that focused on in-program attrition and were published in English between 1985 and 2010 were included in the meta-analysis. Several variables distinguished treatment completers from dropouts, including employment, age, income, education, marital status, race, referral source, previous domestic violence offenses, criminal history, and alcohol and drug use. Furthermore, the theoretical orientation of the treatment program (i.e., feminist psychoeducational vs. cognitive-behavioral therapy) was found to be an important moderating variable. Findings suggest that the variables that predict attrition tend to be the same variables that predict recidivism and are discussed in relation to the responsivity principle.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.017 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".