Process description of developing HIV prevention monitoring indicators for a province-wide pre-exposure prophylaxis (PrEP) program in British Columbia, Canada
Bibliographic record
Abstract
In 2018, the pre-exposure prophylaxis (PrEP) program was initiated in British Columbia (BC), Canada, providing PrEP at no cost to qualifying residents. This observational study discussed the steps to develop key evidence-based monitoring indicators and their calculation using real-time data. The indicators were conceptualized, developed, assessed and approved by the Technical Monitoring Committee of representatives from five health authority regions in BC, the BC Ministry of Health, the BC Centre for Disease Control, and the BC Centre for Excellence in HIV/AIDS. Indicator development followed the steps adopted from the United States Centers for Disease Control and Prevention framework for program evaluation in public health. The assessment involved eight selection criteria: data quality, indicator validity, existing scientific evidence, indicator informativeness, indicator computing feasibility, clients' confidentiality maintenance capacity, indicator accuracy, and administrative considerations. Clients' data from the provincial-wide PrEP program (January 2018-December 2020) shows the indicators' calculation. The finalized 14 indicators included gender, age, health authority, new clients enrolled by provider type and by the health authority, new clients dispensed PrEP, clients per provider, key qualifying HIV risk factor(s), client status, PrEP usage type, PrEP quantity dispensed, syphilis and HIV testing and incident cases, and adverse drug reaction events. Cumulative clients' data (n = 6966; 99% cis-gender males) identified an increased new client enrollment and an unexpected drop during the COVID-19 pandemic. About 80% dispensed PrEP from the Vancouver Coastal health authority. The HIV incidence risk index for men who have sex with men score ≥10 was the most common qualifying risk factor. The framework we developed integrating indicators was applied to monitor our PrEP program, which could help reduce the public health impact of HIV.
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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 | 0.029 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".