Queering Intimate Partner Violence (IPV): An Integrative Review Recognizing Queer Women Experiences and Other Nursing Considerations
Bibliographic record
Abstract
Intimate partner violence (IPV) is a devastating global health issue that impacts more than 1 in 3 women within their lifetime. Health outcomes of IPV can disproportionately impact the LGBTQIA2S+ communities, specifically queer women, including increased rates of anxiety, depression, chronic pain, and disability. Risk factors for help-seeking among queer women experiencing IPV, further known as Queer Women-IPV, are complex, and encompass heteronormative and cis-normative assumptions, discrimination, isolation, and stigma within healthcare, including by nurses. Using Whittemore and Knafl's framework for an integrative review, this review identified unique characteristics of Queer Women-IPV. Furthermore, an examination of how stigma and other discriminations impact healthcare access, and recommendations for nursing professionals are provided to ensure empowering care for Queer Women-IPV survivors. Main themes from the findings included differences in Queer Women-IPV tactics and power dynamics; Queer Women-IPV risk factors; health and quality of life outcomes; as well as the influence of stigma on help-seeking behaviors. Nursing has a significant role in ensuring that queer women have safer places and people to turn to. Recommendations for nursing practice and policy include integrating trauma and violence-informed care as universal standards, offering inclusive holistic screening, and instilling LGBTQIA2S+ education for nurses.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".