Climate Change-Related Risks of Gender-Based Violence (GBV) Among 2SLGBTQIA+ University Students and Emergent Adults: A Scoping Review
Bibliographic record
Abstract
A scoping review was conducted using international databases, including Web of Science, Scopus, ProQuest, PubMed, Embase, and EBSCOhost, covering studies since 2009. Sixty-three articles focusing on gender-based violence (GBV) among 2SLGBTQIA+ university students and emergent adults were analyzed, incorporating climate change-related vulnerabilities that exacerbate GBV risks for marginalized students. Key factors were categorized into bullying, violence, and victimization; intersectionality; lack of awareness; disclosure of violence; and well-being and mental health implications. The findings reveal that discrimination, lack of support, and structural inequalities heighten vulnerability to GBV, compounded by climate-induced stressors such as displacement and resource scarcity. Practical implications include integrating intersectional approaches, tailored mental health support, climate resilience strategies, and anti-discrimination training into institutional policies, while public policy should strengthen safety nets, improve housing and healthcare access, and address compounded risks for marginalized groups during climate crises. Social work should prioritize culturally competent, trauma-informed interventions and foster community resilience. The study identifies critical research gaps, emphasizing the need to expand beyond US-focused studies to explore global intersections of GBV, climate change, and marginalized identities. These findings underscore the urgency of comprehensive strategies to mitigate GBV risks and enhance resilience for 2SLGBTQIA+ students.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".