Technology-facilitated gender-based violence against women with disabilities in low- and middle-income countries: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Women with disabilities in low- and middle-income countries (LMICs) face heightened risks of experiencing gender-based violence (GBV). The rapid growth of digital technologies has introduced new forms of violence, such as technology-facilitated gender-based violence (TFGBV), which disproportionately affects marginalised groups. Despite growing awareness, the intersection of disability, gender and TFGBV is under-researched. This scoping review aims to map and synthesise the evidence on TFGBV against women with disabilities in LMICs, exploring the manifestations of violence, its key vulnerabilities and protective factors within these settings. METHODS AND ANALYSIS: This scoping review will be conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analysis extension for Scoping Reviews guidelines. A systematic search of peer-reviewed and grey literature will be performed in six databases, including CINAHL, Scopus, Web of Science, Social Sciences Citation Index, PubMed and PsycINFO. Studies published from 2010 onwards, focusing on women with disabilities in LMICs and involving any form of TFGBV, will be included. The search strategy includes broad query terms to capture diverse experiences of TFGBV. The identified literature will be screened and double-checked for relevance by independent reviewers. Data extraction will focus on key themes such as study design, forms of TFGBV and the risks and protective factors reported. We will conduct basic content analysis, and results will be presented in tables and narratives, providing a descriptive map of the evidence. ETHICS AND DISSEMINATION: This review will synthesise previously published studies and publicly available grey literature; therefore, ethical approval is not required. The findings will be disseminated through a peer-reviewed publication, presentations at relevant conferences and knowledge-sharing sessions with stakeholders working in the field of disability and GBV prevention. The review will inform future research and interventions aimed at addressing TFGBV in LMICs. TRIAL REGISTRATION DETAILS: Open Science Framework (https://doi.org/10.17605/OSF.IO/GZ2UR).
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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 | 0.115 | 0.087 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.024 | 0.016 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.064 | 0.016 |
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".