A two-day workshop reviewing Canadian provincial and national HIV care cascade indicators, reporting, challenges, and recommendations
Bibliographic record
Abstract
BACKGROUND: The HIV care cascade is an indicators-framework used to assess achievement of HIV clinical targets including HIV diagnosis, HIV care initiation and retention, initiation of antiretroviral therapy, and attainment of viral suppression for people living with HIV. METHODS: The HIV Care Cascade Research Development Team at the CIHR Canadian HIV Trials Network Clinical Care and Management Core hosted a two-day virtual workshop to present HIV care cascade data collected nationally from local and provincial clinical settings and national cohort studies. The article summarizes the workshop presentations including the indicators used and available findings and presents the discussed challenges and recommendations. RESULTS: Identified challenges included (1) inconsistent HIV care cascade indicator definitions, (2) variability between the use of nested UNAIDS's targets and HIV care cascade indicators, (3) variable analytic approaches based on differing data sources, (4) reporting difficulties in some regions due to a lack of integration across data platforms, (5) lack of robust data on the first stage of the care cascade at the sub-national level, and (6) inability to integrate key socio-demographic data to estimate population-specific care cascade shortfalls. CONCLUSION: There were four recommendations: standardization of HIV care cascade indicators and analyses, additional funding for HIV care cascade data collection, database maintenance and analyses at all levels, qualitative interviews and case studies characterizing the stories behind the care cascade findings, and employing targeted positive-action programs to increase engagement of key populations in each HIV care cascade stage.
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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 | 0.211 | 0.264 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.014 | 0.012 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".