Men's Behaviour Change Interventions with Fathers Who Use Violence: The Impact of the Caring Dads Program on Parental Alliance, Family Functioning and Wellbeing
Bibliographic record
Abstract
Men are the predominant perpetrators of domestic and family violence (DFV) with the risk of serious harm disproportionally borne by women and children. In Australia, men’s behaviour change programs (MBCP) form the most significant nationally auspiced response for abusive and violent men, beyond legal sanctions. This one-size-fits-all approach makes it difficult to determine efficacy and application to the diversity of intervention needs, or to establish suitability across a diverse perpetrator population. More broadly across Western contexts, responses are largely siloed across child protection and specialist DFV services, leading to a lack of holistic intervention. Often fathers have remained invisible or minimally engaged where legal sanctions against them are not taken. Yet, due to the complexities of family life, including the child custody rights of fathers, many families have ongoing contact with perpetrators. Recently emerging father-oriented programs have therefore sought to target domestically violent behaviour and poor parenting practice together, leveraging men’s motivation to be better fathers. This study focuses on Caring Dads, a father-focused MBCP program, originating in Canada, which seeks to address the problem of violence in families resulting from both partner and child-directed abuse. The Caring Dads program has some evidence of effectiveness in Australian-comparative contexts, though the focus of evaluations has, to date, centred on improvements to mother and child safety. Such evaluations follow a gendered analysis of violence, which is important for identifying the program’s capacity to create improvements in the safety of women and children, but they do not address the concerns of co-parenting and whole-of-family wellbeing, which are significant when families continue contact in the wake of DFV. This thesis addresses a gap in the evidence by considering whether the Caring Dads program can improve parental alliance and family functioning for families where fathers have used DFV and continue contact with their families, either through remaining in families, or through post-separation parenting contact.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".