Process Monitoring of an HIV Treatment as Prevention Program in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: In light of accumulated scientific evidence of the secondary preventive benefits of antiretroviral therapy, a growing number of jurisdictions worldwide have formally started to implement HIV Treatment as Prevention (TasP) programs. To date, no gold standard for TasP program monitoring has been described. Here, we describe the design and methods applied to TasP program process monitoring in British Columbia (BC), Canada. METHODS: Monitoring indicators were selected through a collaborative and iterative process by an interdisciplinary team including representatives from all 5 regional health authorities, the BC Centre for Disease Control (BCCDC), and the BC Centre for Excellence in HIV/AIDS (BC-CfE). An initial set of 36 proposed indicators were considered for inclusion. These were ranked on the basis of 8 criteria: data quality, validity, scientific evidence, informative power of the indicator, feasibility, confidentiality, accuracy, and administrative requirement. The consolidated list of indicators was included in the final monitoring report, which was executed using linked population-level data. RESULTS: A total of 13 monitoring indicators were included in the BC TasP Monitoring Report. Where appropriate, indicators were stratified by subgroups of interest, including HIV risk group and demographic characteristics. Six Monitoring Reports are generated quarterly: 1 for each of the regional health authorities and a consolidated provincial report. CONCLUSIONS: We have developed a comprehensive TasP process monitoring strategy using evidence-based HIV indicators derived from linked population-level data. Standardized longitudinal monitoring of TasP program initiatives is essential to optimize individual and public health outcomes and to enhance program efficiencies.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".