Summary findings from Tracks surveys implemented by First Nations in Saskatchewan and Alberta, Canada, 2018–2020
Bibliographic record
Abstract
Background: The Public Health Agency of Canada's integrated bio-behavioural surveillance system-Tracks surveys-assesses the burden of HIV, hepatitis C and associated risks in key populations in Canada. From 2018-2020, Tracks surveys were successfully implemented by First Nations Health Services Organizations in Alberta and Saskatchewan. Methods: First Nations-led survey teams invited community members who identified as First Nations, Inuit or Métis to participate in Tracks surveys and testing for HIV, hepatitis C and syphilis. Information was collected on social determinants of health, use of prevention services, substance use, sexual behaviours and care for HIV and hepatitis C. Descriptive statistics are presented. Results: Of the 1,828 survey participants, 97.4% self-identified as First Nations and 91.4% lived in an on-reserve community. Over half (52.2%) were cisgender female, average age was 36.3 years, 82.5% lived in stable housing, 82% had access to primary healthcare and 73.8% reported having good to excellent mental health. Most participants (97%) had a family member who had experienced residential school. High proportions experienced stigma and discrimination (65.6%), financial strain (64.3%) and abuse in childhood (65.1%). Testing for HIV (62.8%) and hepatitis C (55.3%) was relatively high. Prevalence of HIV was 1.6% (of whom 64% knew their infection status). Hepatitis C ribonucleic acid prevalence was 5% (44.9% of whom knew their current infection status). Conclusion: Historical and ongoing experiences of trauma, and higher prevalence of hepatitis C were identified, reaffirming evidence of the ongoing legacies of colonialism, Indian Residential Schools and systemic racism. High participation in sexually transmitted blood-borne infection testing and prevention reflect the importance of First Nations-led culturally sensitive, safe and responsive healthcare services and programs to effect improved outcomes for First Nations peoples.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".