Repositioning Implementation Science in the HIV Response: Looking Ahead From AIDS 2018
Bibliographic record
Abstract
BACKGROUND: Implementation science (IS) occupies a critical place in HIV/AIDS research, reflected by a scientific track ("Track E") at the biannual International AIDS Conference. IS seeks to identify health delivery strategies that cost-effectively translate the efficacy of evidence-based interventions for HIV prevention, testing, and treatment into impact on HIV incidence, quality of life, and mortality. METHOD: We reviewed the content of Track E, and other presentations relevant to IS, at the 22nd International AIDS Conference held in Amsterdam in 2018. We identified key findings and themes and made recommendations for areas where the field can be strengthened by the 2020 meeting. RESULTS: Trials of "treat all" strategies in Africa showed mixed evidence of effect. Innovations in HIV testing included expanding self-testing and index testing, which are reaching groups, such as men, where previously testing rates have been low. Adherence clubs and other innovations are being trialed to improve retention in care, with mixed findings. The implementation of pre-exposure prophylaxis for HIV prevention continues but with many challenges remaining in identifying implementation strategies that strengthen demand and support continuation. DISCUSSION: IS for HIV/AIDS treatment and prevention continues to expand. IS for primary HIV prevention must be prioritized with a dearth of rigorous, intersectoral studies in this area. The weakness of routine data must be addressed. Costing and financing studies should form a stronger component of the conference agenda. Implementation scientists must continue to grapple with the methodological challenges posed by the real-world context for their research.
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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 | 0.220 | 0.260 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.028 | 0.042 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.025 | 0.046 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".