Systemic inequities and sources of resilience: challenges faced by Indigenous women living with HIV during COVID-19 in the Canadian prairies
Bibliographic record
Abstract
This study explored the challenges faced by, and resilience of First Nations, Métis, and Inuit women living with HIV in Manitoba and Saskatchewan during the COVID-19 pandemic. Through a decolonizing, community-based research approach, guided by a Community Guiding Circle (CGC), interviews were conducted with 45 Indigenous women living with HIV. Participants were recruited via community outreach, peer networks, and social media. Data collection and analysis utilized Indigenous storywork and inductive thematic analysis. The study revealed significant barriers related to housing instability, childcare, and access to healthcare, all exacerbated by systemic inequities rooted in colonialism, patriarchy, and capitalism. Despite these challenges, Indigenous cultural practices, ceremonies, community support, and family ties emerged as crucial sources of resilience, though often disrupted during the pandemic. The findings underscore the urgent need for culturally safe, women-centered care models that integrate Indigenous knowledge and practices. For health and social care practitioners, this research emphasizes the importance of advocating for systemic change to address the unique needs of Indigenous women living with HIV and calls for the development and implementation of culturally safe health and social care tailored to their unique needs and resilience.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".