Exploring community- and systemic-level gender-based violence in visible minority women across five countries from an intersectionality lens: protocol for a mixed-methods systematic review
Bibliographic record
Abstract
INTRODUCTION: The intersection of sexism with racism and xenophobia disproportionately exposes visible minority women to gender-based violence (GBV) at the community and systemic levels. This study aims to understand the knowledge strengths and gaps on GBV against visible minority women with an intersectional lens, revealing systemic barriers to accessing support and how these barriers intensify GBV and its effects. It will also identify effective and ineffective policies and practices in the literature to develop strategies addressing the root causes of GBV and supporting survivors. METHODS AND ANALYSIS: We will conduct a mixed-methods systematic review using a convergent integrated approach to examine current literature on community- and systemic-level GBV against visible minority women. We will follow Joanna Briggs Institute's guidelines to converge data from both qualitative and quantitative studies to obtain an integrated qualitative synthesis on GBV in five countries: Canada, the USA, the UK, Australia and New Zealand. This analysis will be conducted following Thomas and Harden's thematic synthesis guidelines. Community members with lived experience of GBV will actively contribute to improving the relevance and interpretation of results, following a community-engaged research approach. Themes are expected to unveil various aspects of community- and systemic-level GBV due to the intersection of racism, xenophobia and sexism, alongside barriers in addressing GBV and research gaps. ETHICS AND DISSEMINATION: Since this study does not involve primary data collection or the use of identifiable human data, no ethical approval will be needed. Results will be disseminated through integrated knowledge translation, involving collaboration with participants who have lived experience of GBV. The findings will be used to identify specific areas of policy intervention, including adopting culturally sensitive approaches, improving school and workplace policies and promoting rights of visible minority women.
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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 | 0.150 | 0.157 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.047 | 0.011 |
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".