SSHRC Evidence Brief: Gender-Based Violence Against Immigrants and Refugees Living with HIV/HIV-Risk in Canada: A Systematic Review
Bibliographic record
Abstract
Immigrant and refugee women in Canada bear a disproportionate burden of HIV and HIV risk, and simultaneously experience a greater risk of gender-based violence (GBV), including interpersonal, community, and structural violence. The observed increase in multiple forms of GBV during the recent COVID-19 pandemic underscores the urgent need for research-informed progressive policies, practices, and community services to support this population. Understanding the impact of systemic racism and sexism within the context of immigrant and refugee women’s dual experiences of HIV/HIV-risk and GBV (HIV/GBV) is necessary to effectively develop comprehensive strategies that can challenge structural barriers and promote equity, social inclusion, and psycho-social well-being. This project sought to understand: • How systemic racism and sexism impact immigrant and refugee women’s dual experiences of HIV/GBV. • What policies, programs, or services, or lack there-of, support or create barriers for immigrant and refugee women experiencing HIV/GBV and what changes are required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".