Intersectionality and sex and gender-based analyses as promising approaches in addressing intimate partner violence treatment programs among LGBT couples: A scoping review
Bibliographic record
Abstract
Although Intimate Partner Violence (IPV) is an important health and social issue, less is known about IPV among sexual orientation and gender-minoritized (SOGI) populations such as Lesbian, Gay, Bisexual and Transgender (LGBT) couples. IPV among same-sex (e.g. lesbian, gay, bisexual) and gender-minoritized (e.g. transgender) couples requires a reframing of this issue from a heteronormative and cisnormative lens in order to better understand and effectively address approaches to prevent this kind of abuse and to improve treatment programs. The purpose of this scoping review is to explore why including an intersectional lens in Sex and Gender-Based Analysis is needed to improve effectiveness of IPV treatment programs, analyzing what works and why among SOGI populations impacted by IPV in current IPV programs. Specifically, this scoping review systematically searched three academic databases to identify peer-reviewed publications examining: (a) existing treatment programs for SOGI-minoritized populations who are impacted by IPV, and (b) suggestions for future policies and services for SOGI-minoritized populations. Of the 1172 potential articles, 75 met the inclusion criteria, but none described IPV programs specific to SOGI-populations. The findings of this scoping review reflect the need for developing IPV programs that are informed by evidence-based practice in health and social services for SOGI populations, and will offer new approaches for current BIPs programs to move forward prevention and intervention.
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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 | 0.021 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".