Determinants of nonphysical intimate partner violence: a cross-sectional study with nationally representative data from Canada
Bibliographic record
Abstract
Based on a nationally representative survey of the Canadian population conducted in 2019/2020, this study examined the prevalence and determinants of nonphysical intimate partner violence (NP-IPV). NP-IPV was defined as experiences of emotional abuse, controlling behaviors, or economic abuse during the past 5 years. Women (17.3%) and men (16.9%) were equally likely to report NP-IPV, often without co-occurring physical/sexual IPV. For both genders, the risk of NP-IPV decreased with age and increased with financial strain and having a disability. Childhood maltreatment (sexual abuse, emotional abuse, and exposure to emotional IPV for women and sexual abuse and emotional abuse for men) was associated with an increased likelihood of reporting NP-IPV in adulthood. Other risk factors included heavy episodic drinking (self and/or spouse/partner) for women and living in a neighborhood with high levels of social disorder for men. Having confidence in the police was a protective factor for both genders. These associations generally persisted in regression analyses controlling for co-occurring physical/sexual IPV. Future research should focus on methods of preventing NP-IPV and the development of gender-specific interventions to reach and support those who experience NP-IPV. Furthermore, there is a need to improve and standardize measures of NP-IPV.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".