"I Used to be Scared to Even Like Stand Beside Somebody Who Had It": HIV Risk Behaviours and Perceptions among Indigenous People Who Use Drugs
Bibliographic record
Abstract
Objectives: In Canada, and elsewhere, Indigenous people who use illicit drugs and/or alcohol (WUID/A) experience a disproportionate burden of HIV-related harm. This study examined HIV risk perceptions and behaviours among Indigenous people WUID/A living in the Downtown Eastside (DTES) and the policies and practices that shape inequities and vulnerabilities for them in HIV testing and treatment. Further, we aimed to situate the vulnerabilities of Indigenous people WUID/A in HIV care within the context of wider structural inequality and generate recommendations for culturally relevant and safe HIV treatment options. Methods: This research employed an Indigenous-led community-based participatory approach using talking circles to explore experiences of Indigenous people living with HIV. Under the participatory research framework, community researchers led the study design, data collection, and analysis. Talking circles elicited participants’ experiences of HIV knowledge, testing, and treatment, and were audio-recorded and transcribed. Data were coded line-by-line and codes were organized into themes. Results: Five key themes were identified via the talking circles: evolving HIV risk perceptions (e.g., HIV knowledge and testing, and “intentional exposure”); research as an avenue for HIV testing; HIV treatment and discussions about grief and loss; HIV-related stigma and discrimination; and the importance of culturally-relevant and safe HIV treatment options for Indigenous people WUID/A. Discussion: Our work reveals that Indigenous people WUID/A do not have adequate access to HIV knowledge and education, often limiting their ability to access HIV testing and supports. Participant stories revealed both internalized and community stigma and discrimination, which at times compromised connection with participants' home communities. Further, our findings point to a failure in the public health system to deliver accessible HIV information to Indigenous Peoples, hence, many participants have solely relied on participation in community-based research studies in the Downtown Eastside (DTES) for HIV education and knowledge. There is an urgent need for accessible, culturally safe, and community-based education and treatment options for Indigenous people WUID/A within HIV care.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".