Psychosocial Consequences of Intimate Partner Violence for Women and Men in Canada
Bibliographic record
Abstract
Although the negative health consequences of intimate partner violence (IPV) are well documented, most research has conceptualized IPV as a unitary construct and has primarily focused on the impact of physical violence. However, recent theoretical and empirical work suggests that IPV may be heterogeneous, with different consequences associated with different patterns of violence, abuse, and control. This study used latent class analysis to examine the psychosocial consequences associated with different patterns of physical violence, sexual coercion, psychological abuse, and controlling behavior. Data from 676 women and 455 men who were interviewed for the 2004 Canadian General Social Survey on Victimization were analyzed. The results suggest that experiencing any pattern of violence is associated with a range of negative psychosocial outcomes for both women and men. However, they also show the increasingly negative impact and perceived dangerousness of IPV for those experiencing more severe and chronic patterns of violence and control. These findings were particularly pronounced for women as they experienced the most chronic pattern of abuse and control documented in the study. The psychosocial consequences were also greater for women than for men with similar experiences of IPV. These results suggest that the psychosocial impact of IPV is influenced by gender and by the nature of the violence, abuse, and control experienced.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".