Barriers and recommendations for harm reduction services among people living with HIV in Manitoba, Canada: a qualitative study
Bibliographic record
Abstract
Introduction: In 2022, the province of Manitoba, Canada, recorded its highest increases in substance-related deaths and new HIV diagnoses. The COVID-19 pandemic exacerbated access barriers to harm reduction services across the country. Given the intertwined relationship between HIV and injection substance use, we sought to better understand People Living with HIV's (PLHIV) access barriers to harm reduction services, and recommendations for improved care. Methods: This qualitative study was co-designed by and co-executed with people with lived experiences in HIV and substance use. The data collection process encompassed a semi-structured in-depth qualitative interview with PLHIV followed by three quantitative questionnaires and was conducted between October 2022 and May 2023 in HIV clinics. Descriptive statistics were performed to illustrate substance use practices, and we employed reflexive thematic analysis to generate themes and explain shared patterns of meaning across participants in relation to our research question. Results: . In the first theme, participants described being aware of the different harm reduction services in their community, but recounted several access barriers limiting service uptake, including restrictive service times and limited mobile services. These limitations increased participants' likelihood of sharing injecting equipment, and produced stress and anxiety about lacking access to safe supplies. In the second theme, participants discussed what they consider "safe" spaces for using substances, highlighting the importance of autonomy and privacy where they can use without fear of stigma and interference. Thus, to make substance use safer in Manitoba, participants advocated for the implementation of supervised consumption sites to ensure the availability of non-judgmental spaces where they can find and use safe injecting supplies, trained staff, and connections to health and social supports. Discussion: PLHIV who use substances face many hurdles when seeking harm reduction and health services. It is essential to implement new strategies centred on the lives of PLHIV who use substances to address the unprecedented rates of HIV diagnoses, health-related harms, and substance-related deaths.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.028 | 0.009 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".