Scoping review on technology-facilitated gender-based violence against women with disabilities and LGBTQI+ persons in low- and middle-income countries
Bibliographic record
Abstract
Background: Technology-facilitated gender-based violence (TFGBV) refers to acts of harm enabled or intensified through digital platforms, negatively affecting women’s rights, safety and well-being. Women with disabilities are at heightened risk because of the intersection of ableism and gender inequality. However, limited evidence exists on how TFGBV manifests and impacts this population, particularly in low- and middle-income countries (LMICs). Objectives: This scoping review aimed to map existing research on TFGBV against women with disabilities in LMICs, identify common patterns and explore intersections with broader structural vulnerabilities. Method: Using the PRISMA-ScR framework, we searched seven academic databases and grey literature published between 2010 and 2024. Eligible studies focused on women in LMICs and involved TFGBV through consumer digital technologies. Data were charted and deductively analysed using adapted frameworks from prior TFGBV literature. Results: From 4738 records screened, 43 studies met the inclusion criteria. Most explored how digital tools enabled violence with offline consequences. None focused exclusively on women with disabilities, though some included them. Technology-facilitated gender-based violence impacts were wide-ranging, with LGBTQ+ individuals, rural populations and low-income groups facing intersecting risks. Conclusion: This review highlights a gap in research on TFGBV among women with disabilities in LMICs. Future studies must centre intersectional, inclusive and survivor-informed approaches. Contribution: This review adopted an intersectional approach, recognising how disability, gender, poverty and other marginalised identities compound TFGBV risks. It highlights the lack of focused research on TFGBV against women with disabilities in LMICs and the need for inclusive, survivor-informed research and policy responses.
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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 | 0.012 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.023 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".