Addressing the Know-Do Gap in Adolescent HIV: Framing and Measuring Implementation Determinants, Outcomes, and Strategies in the AHISA Network
Bibliographic record
Abstract
Implementation science (IS) uses systematic methods to close gaps between research and practice by identifying and addressing barriers to implementation of evidence-based interventions (EBIs). To reach UNAIDS HIV targets, IS can support programs to reach vulnerable populations and achieve sustainability. We studied the application of IS methods in 36 study protocols that were part of the Adolescent HIV Prevention and Treatment Implementation Science Alliance (AHISA). Protocols focused on youth, caregivers, or healthcare workers in high HIV-burden African countries and evaluated medication, clinical and behavioral/social EBIs. All studies measured clinical outcomes and implementation science outcomes; most focused on early implementation outcomes of acceptability (81%), reach (47%), and feasibility (44%). Only 53% used an implementation science framework/theory. Most studies (72%) evaluated implementation strategies. Some developed and tested strategies, while others adapted an EBI/strategy. Harmonizing IS approaches allows cross study learning and optimization of delivery of EBIs and could support attainment of HIV goals.
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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 | 0.119 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".