A Latent Profile Analysis of Intimate Partner Violence Perpetrated by Men Seeking Help
Bibliographic record
Abstract
Past research has emphasized the need to identify profiles of men who perpetrate intimate partner violence (IPV) as a way to better understand this heterogeneous population and guide the development of tailored services. However, empirical validation for such profiles remains limited, since it still focuses on specific populations or fails to consider IPV as reported by men seeking treatment for IPV. We know little about the profiles of men who seek services for their use of IPV (with or without a justice referral). This study sought to identify profiles of men seeking treatment for IPV, based on their self-reported use of the various forms and severity of IPV perpetrated, and to compare the identified groups on key psychosocial risk markers of IPV. A total of 980 Canadian men entering treatment in community organizations specialized in IPV answered a series of questionnaires. A latent profile analysis identified four profiles: (a) “no/minor IPV” ( n = 194), (b) “severe IPV with sexual coercion” ( n = 122), (c) “minor IPV and control” ( n = 471), and (d) “severe IPV without sexual coercion” ( n = 193). Results revealed differences in psychosocial risk markers, including attachment insecurities, childhood interpersonal trauma, undesirable personality traits, affect dysregulation, and psychological distress, mostly between the “severe IPV without sexual coercion” profile and the “no/minor IPV” and “minor IPV and control” profiles. Very few differences were found, however, between the “severe IPV with sexual coercion” and “severe IPV without sexual coercion” profiles. Implications for awareness, prevention, and treatment efforts for each profile are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".