Experiences of Gender-Based Violence Among Disabled Women: A Qualitative Systematic Review and Meta-Synthesis Protocol
Bibliographic record
Abstract
Background Gender-based violence (GBV) is a major public health concern and a human rights issue disproportionately affecting disabled women. Disabled women are twice as likely to experience GBV than nondisabled women, yet there has been a lack of attention to this issue. This review aims to gain a greater understanding of the experiences of GBV of disabled women through a systematic and qualitative meta-synthesis. The qualitative meta-synthesis will be conducted by a research team of academic and community members and students with varying lived and service provider experiences with disabilities and GBV. The study findings aim to promote best practices by offering solutions to increase accessible and inclusive resources and services responsive to disabled women. Methods A systematic review of qualitative studies will be performed based on searches of 12 academic databases, including MEDLINE (Ovid), APA PsycINFO (EBSCO), Sociological Abstracts (ProQuest), Social Services Abstracts (ProQuest), and SocINDEX (EBSCO). A search of the gray literature will be performed by searching the Google search engine, Google Scholar, the Community Health Online Digital Archive and Research Resource (CHODARR), and the Global Database on Violence Against Women. In addition to the database and gray literature searching, we will complete backward and forward citation tracing. Two research team members will be involved in all screening, review, data extraction, and quality assessment of studies. A third reviewer will resolve any disagreements and consult with the research team. Thematic synthesis steps will include becoming familiar with the data, developing a thematic framework, indexing the data to identify themes and codes, charting the data, and mapping and interpreting the data. The Critical Appraisal Skills Programme checklist will be used to appraise the quality of included studies. Confidence in the meta-synthesis findings will be assessed by applying the GRADECERQual approach. Review Registration This protocol is registered with the International Prospective Register of Systematic Reviews (PROSPERO): CRD42023400410.
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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 | 0.163 | 0.165 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.017 | 0.017 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.082 | 0.008 |
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".