Examining Men’s Experiences of Abuse From a Female Intimate Partner in Four English-Speaking Countries
Bibliographic record
Abstract
This qualitative study explores the experiences of men who self-report victimization from a female intimate partner in four English-speaking countries. Forty-one men who reported any type of intimate partner abuse (IPA) from a female partner were recruited via targeted advertising in Australia, Canada, the United Kingdom, and the United States. Twelve online focus groups were conducted across countries using a phenomenologically informed design. Thematic analysis was carried out from an inductive and realist epistemological position and themes identified at a semantic level. This approach was taken to directly reflect the men’s experiences and perspectives, ensuring the voices of this hard-to-reach and overlooked population were heard. Three themes were identified across the countries: an imbalanced experience of harm; living with sustained abuse; and knowledge is power for men experiencing IPA. It was found that most participants underwent physical harm in the context of coercive control and experienced abuse over long periods of time. They were slow to recognize the magnitude of their partners’ behavior and act upon it for a range of reasons that are described in detail. In addition, promoting knowledge about the victimization of men by women, using appropriate language and active learning, was found to be important in helping the men gain autonomy and agency to break the pattern of abuse and aid their recovery. The implications of the findings for developing male-friendly IPA policy, practice, and services are discussed, in addition to the need for innovative research methodology to access hard-to-reach populations.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".