A cross-sectional investigation of HIV prevalence and risk factors among African, Caribbean and Black people in Ontario: The A/C Study
Bibliographic record
Abstract
Background: The human immunodeficiency virus (HIV) epidemic has disproportionately affected African, Caribbean and Black (ACB) communities in Canada. We investigated the prevalence and factors associated with HIV infection among ACB people in Ontario. Methods: A cross-sectional survey of first- and second-generation ACB people aged 15-64 years in Toronto and Ottawa (Ontario, Canada). We collected sociodemographic information, self-reported HIV status and offered dried blood spot (DBS) testing to determine the prevalence of HIV infection. Factors associated with HIV infection were investigated using regression models. Results: A total of 1,380 people were interviewed and 834 (60.4%) tested for HIV. The HIV prevalence was 7.5% overall (95% confidence interval [CI] 7.1-8.0) and 6.6% (95% CI 6.1-7.1) in the adult population (15-49 years). Higher age (adjusted odds ratio [aOR] 2.8; 95% CI 2.77-2.82), birth outside of Canada (aOR 4.7; 95% CI 1.50-14.71), French language (aOR 9.83; 95% CI 5.19-18.61), unemployment (aOR 1.85; 95% CI 1.62-2.11), part-time employment (aOR 4.64; 95% CI 4.32-4.99), substance use during sex (aOR 1.66; 95% CI 1.47-1.88) and homosexual (aOR 19.68; 95% CI 7.64-50.71) and bisexual orientation (aOR 2.82; 95% CI 1.19-6.65) were associated with a positive HIV test. Those with a high school (aOR 0.01; 95% CI 0.01-0.02), college (aOR 0.00; 95% CI 0.00-0.01) or university education (aOR 0.00; 95% CI 0.00-0.01), more adequate housing (aOR 0.85; 95% CI 0.82-0.88), a higher social capital score (aOR 0.61; 95% CI 0.49-0.74) and a history of sexually transmitted infections (aOR 0.40; 95% CI 0.18-0.91) were less likely to have a positive HIV test. Conclusion: Human immunodeficiency virus infection is linked to sociodemographic, socioeconomic, and behavioural factors among ACB people in Ontario.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".