Mental Health Supports for Sexual and Gender Minorities Who Experience Intimate Partner Violence and Abuse: A Systematic Review of North American Literature
Bibliographic record
Abstract
Sexual and gender minorities (SGM) experience inequitable health outcomes due to oppressive social forces (e.g., homophobia, transphobia). For SGM experiencing intimate partner violence and abuse (IPV), help-seeking is challenged by concomitant navigation of the abusive partner and discriminatory forces toward SGM. Mental health providers are an important source of support who can provide SGM experiencing IPV with the tools and resources they need to manage or leave abusive relationships. This systematic review synthesized existing literature on the help-seeking experiences of SGM in North America who experienced IPV and accessed mental health supports. This secondary review (#CRD42020139639) from a larger study used meta-aggregative methods to identify main findings. The authors searched peer-reviewed literature from MEDLINE, Embase, PsycInfo, Scopus, CINAHL, Genderwatch, and Social Science Abstracts and assessed validity using the Joanna-Briggs Institute Checklists. Four synthesized findings were identified from studies ( N = 34) conducted in the United States ( n = 29) and Canada ( n = 5): (a) components of SGM-affirming spaces, (b) characteristics of mental health provision, (c) healing journey process, and (d) community awareness and familiarity with services. Mental health providers (not including couples’ counselors) were identified as positive forms of support for SGM experiencing IPV, providing opportunities for self-development and skills to manage or leave abusive relationships. SGM-affirming spaces (e.g., use of inclusive language) increased SGM comfort in IPV discussions with mental health providers. Training and acknowledgment of SGM-specific IPV, along with client-provider rapport, further underlie effective mental health supports. Increased education and outreach would promote improved access to mental health supports.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".