Engaging Men in Intimate Partner Relationship Programs: Service Provider and Stakeholder Perspectives
Bibliographic record
Abstract
Men’s intimate partner relationship services have focused on correcting the behaviors of male perpetrators of intimate partner (IPV) and/or domestic violence (DV). There is a need to advance IPV and DV prevention efforts by better equipping men with relationship skills. This study explores service providers’ and stakeholders’ perspectives about the challenges and strategies for assisting men to build better intimate partner relationships. Interviews were conducted with participants ( n = 30) from Canada and Australia who worked in the men’s intimate partner relationships sector. Three themes were inductively derived: (a) crisis management (barriers to engagement), (b) owning deficits and leveraging strengths (engaging though accountability and action), and (c) me then we (self-work as requisite for relationship success). Using a gender relations lens, we examined the influence of masculinities on men’s intimate partner relationships and engagement with services. Participants described crisis management challenges for men accessing services including shame, threats to masculine identity, and mental health challenges. Owning deficits and leveraging strengths hinged on men’s accountability and action, rather than assigning blame for problematic behaviors in accessing services. Related to this, the me then we theme highlighted men’s strength-based approaches in focussing on self-work to develop tangible skills and awareness needed to build healthy relationships. Overall, the findings indicate men’s healthy relationships hinged on working with masculine identities to inform their perspectives and behaviors. Men’s intimate partner relationship work likely requires labor at multiple levels (e.g., individual, partners, and systems) to secure the strong potential for reframing masculine identities as asset-building for men’s relationships.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".