Intimate partner violence: Perseverance vs Dropout from treatment programs targeting male batterers
Bibliographic record
Abstract
Introduction: The Dropout phenomenon has been studied extensively in general psychotherapy and in more specific domains. Therapeutic programs for male batterers across Europe, United States or Canada also have their share of problems in their attempts to maintain users in treatment. The purpose of this study is to identify potential intrapsychic variables associated with dropout and completion of this kind of treatment. Method: Fifty one male offenders enrolled in a group treatment for domestic violence took part in the study (23 who abandoned and 28 who completed). Before the start of therapy, they were surveyed using questionnaires and structured clinical interview to collect sociodemographic data and to assess specific psychological variables, namely emotional distress (BDI), impulsive behavior (BIS-11), early relationship with their parents (PBI), life events (LEDS) and alexithymia (TAS-20), the inability to experience and express subjective emotions. Results: Correlational analysis showed that age, paternal parenting behaviors and the number of significant events reported in the past six months are the only variables correlated with treatment dropout. The younger the participants, the most likely they were to dropout of the group therapy. Moreover, men who completed treatment reported fewer difficulties in their life and less autonomy support by their father than men who prematurely terminated their therapy. Discussion: The present findings pose a serious challenge for all actors involved in specifics treatment programs. Indeed, if all individuals who committed violent acts have the same intrapsychic variables, how do we predict, especially retain and motivate, the users likely to dropout?
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".