A way forward with intersectionality: An integrative review of women’s experiences with intimate partner violence
Bibliographic record
Abstract
Intimate partner violence is uniquely posited as both a public health challenge and an affront to human rights that spares no social group with women, as victims, constitute two thirds of all instances worldwide. Intimate partner violence is a ‘wicked’ problem that requires an upstream approach that demonstrates awareness of the multitude of intersections experienced by women. This study utilized an integrative review methodology to examine the application of intersectionality as a framework to women’s experiences of intimate partner violence. The search identified primary research papers from five databases during March 2022 and April 2024. A total of 3123 articles were distilled into a final five articles. The major themes included: (a) returning to the roots of intersectionality; (b) intersections of women’s lived experience; and (c) intersections with intimate partner violence. The need for an intersectional approach to IPV is agreed upon and, given the urgency of this issue, the findings establish a way forward for intersectional research and presents connections between intersectionality and IPV. This integrative review underscores the critical need for integrating intersectionality into both research and interventions to address intimate partner violence, ensuring that responses are inclusive, nuanced, and effectively tailored to the diverse realities of women’s lived experiences.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.024 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".