Good news on HIV: Tackling the social and structural drivers of HIV in Canada
Bibliographic record
Abstract
There is new hope that we can significantly reduce HIV rates. The United Nations AIDS organization, UNAIDS, has challenged all countries to strive for aggressive targets that could significantly bend the curve on HIV infections and deaths: 90% of people living with HIV diagnosed; 90% of people diagnosed on treatment; and 90% of people on treatment virally suppressed. This new optimism is largely driven by strong research findings that early and ongoing HIV treatment improves individual health outcomes and reduces people’s viral load, making them less infectious. However, the risk of HIV infection is far from evenly distributed among populations most at risk. Those most at risk will find it hardest to reach these targets as they are caught in a syndemic (synergistic epidemic) of intertwining health and social issues. Our research, and that of others, shows that those who are in a syndemic of co-occurring mental health, addiction and social issues (e.g. homelessness, food insecurity) are significantly more likely to fall out of care, less likely to adhere to treatment and less likely to achieve/maintain an undetectable viral load. Intervention studies have found that a combination approach to HIV prevention and treatment that goes beyond primary care and mental health tools to include social and structural interventions has a protective effect, and can reduce risk and improve adherence. People living with and at risk of HIV need better access to social and mental health services as well as clinical treatment services that will help them achieve and maintain optimal health and well-being. We strongly encourage those in the HIV sector across the country to identify a common vision, with clear goals and targets. With concerted and targeted efforts, a focus on program and implementation science, and a willingness to see and treat HIV as a social as well as a biomedical problem—the fourth decade of HIV in Canada could well be the last.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 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".