Challenging the HIV Epidemic in Ontario Through PrEP and HIV Testing
Bibliographic record
Abstract
The Ontario HIV Treatment Network (OHTN) is a non-profit network which collaborates with health clinics, AIDS service and community organizations, and policy leaders in order to improve the health and wellbeing of people living with and at risk of HIV. I joined the OHTN as a member of the Collective Impact team, with a focus on examining the barriers and facilitators to Pre-Exposure Prophylaxis (PrEP) uptake in Ontario. PrEP is a once-daily pill which is highly effective in preventing HIV infections for HIV-negative people, however usage remains relatively low in Ontario. In this role, I liaised with the Knowledge Synthesis team at OHTN to collect, analyse, and synthesize recent scientific literature on Pre and Post-exposure prophylaxis (PEP) in order to create a comprehensive annotated bibliography on PrEP research. Key findings were drawn from the research to identify potential next steps to increase PrEP use for priority populations in Ontario. Findings from the annotated bibliography were presented to OHTN staff, and have been used to assist in the development of two PrEP study proposals; 1) a cisgender and transgender women-focused PrEP education package and HIV risk screening tool, and 2) a pharmacist-led PrEP delivery pilot. I also worked with the Testing and Clinical Initiatives team at the OHTN, to aid in the implementation and evaluation of two HIV-testing projects: the GetaTest pharmacy-based HIV-testing study, and the GetaKit HIV self-testing pilot program. In this role I drafted health communication materials; analysed survey data and drafted project reports for stakeholders; and provided perspectives on the HIV-care continuum, particularly on PrEP initiation, adherence, and efficacy. My work with the OHTN was important to public health because it sought to expand access to HIV testing and prevention services for priority populations in Ontario, including men who have sex with men, and cis and trans women.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".