Findings among Indigenous participants of the Tracks survey of people who inject drugs in Canada, Phase 4, 2017–2019
Bibliographic record
Abstract
BACKGROUND: The Tracks survey of people who inject drugs (PWID) collected data in 14 sentinel sites across Canada (2017-2019). These findings describe the prevalence of human immunodeficiency virus (HIV), hepatitis C and associated risk behaviours among Indigenous participants. METHODS: Information regarding socio-demographics, social determinants of health, use of prevention services and testing, drug use, risk behaviours, and HIV and hepatitis C testing, care and treatment was collected through interviewer-administered questionnaires. Biological samples were tested for HIV, hepatitis C antibodies and hepatitis C ribonucleic acid (RNA). Descriptive statistics were calculated and reviewed by an Indigenous-led advisory group using the Two-Eyed Seeing approach. RESULTS: Of the 2,383 participants, 997 were Indigenous (82.9% First Nations, 14.9% Métis, 2.2% Inuit). Over half (54.5%) were cisgender male and the average age was 38.9 years. A large proportion (84.0%) reported their mental health as "fair to excellent". High proportions experienced stigma and discrimination (90.2%) and physical, sexual and/or emotional abuse in childhood (87.5%) or with a sexual partner (78.6%). Use of a needle/syringe distribution program (90.5%) and testing for HIV (87.9%) and hepatitis C (87.8%) were high. Prevalence of HIV was 15.4% (78.2% were aware of infection status) and 36.4% were hepatitis C RNA-positive (49.4% were aware of infection status). CONCLUSION: High rates of HIV and hepatitis C were identified. Challenges in access to and maintenance of HIV and hepatitis C care and treatment were noted. This information informs harm reduction strategies, including the need to scale-up awareness of prophylaxis in a culturally relevant manner.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".