Decolonising the HIV Care Cascade: Policy and Funding Recommendations from Indigenous Peoples Living with HIV and AIDS
Bibliographic record
Abstract
Indigenous Peoples in settler colonial nations, like Canada, continue to experience the intergenerational trauma, racism, socioeconomic disadvantages, and pervasive health disparities resulting from centuries of systemic oppression. Among these is the disproportionate burden of HIV in Canada’s Indigenous population, coupled with a lack of access to care and services. One method of assessing systems-level gaps is by using the HIV care cascade, whereby individuals are diagnosed, antiretroviral treatment is initiated, and viral suppression is achieved and maintained. The cascade, as it stands today, does not yield positive outcomes for Indigenous Peoples living with HIV. In order to close existing gaps, the authors sought to decolonise the HIV care cascade by rooting it in funding and policy recommendations provided directly by Indigenous Peoples living with HIV. This research presents 29 recommendations that arose when First Nations participants living with HIV partook in traditional storytelling interviews to share their life’s journey and offer suggestions for improving access to care and services. Said recommendations are to localize testing and diagnosis (while upholding confidentiality), improve access to culturally-appropriate care and services, provide targeted programming for Indigenous women and heterosexual men, and increase funding for provincial disability benefits; important steps in decolonising the HIV care cascade.
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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 | 0.029 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".