Exploring gender differences in the patterns of intimate partner violence in Canada: a latent class approach
Bibliographic record
Abstract
BACKGROUND: There has been an ongoing debate about the extent and nature of gender differences in the experience of intimate partner violence (IPV). Disagreement about the appropriate definition of IPV is central to this debate. METHODS: This study used latent class analysis (LCA) to map the patterns of physical violence, sexual coercion, psychological abuse and controlling behaviour, and examined whether LCA can better illuminate the gendered nature of this experience than conventional measures of IPV. Data from the 2004 Canadian General Social Survey were analysed, which included 8360 women and 7056 men 15 years of age and over who reported a current or ex-spouse or common-law partner. RESULTS: Results revealed more variation in the patterns of IPV for women than for men. Six classes were found for women, whereas four classes were found for men. Women and men were equally likely to experience less severe acts of physical aggression that were not embedded in a pattern of control. However, only women experienced a severe and chronic pattern of violence and control involving high levels of fear and injury. For women and men, intermediate patterns of violence and control, and patterns describing exclusively non-physical acts of abuse were also found. The results also revealed substantial differences in the IPV subtypes for those reporting about a current versus an ex-partner. CONCLUSION: These results support the use of LCA in identifying meaningful patterns of IPV and provide a more nuanced understanding of the role of gender than conventional measures. Implications for sampling within IPV research are discussed.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".