Using the Implementation Research Logic Model as a Lens to View Experiences of Implementing HIV Prevention and Care Interventions with Adolescent Sexual Minority Men—A Global Perspective
Bibliographic record
Abstract
Adolescents and sexual minority men (SMM) are high priority groups in the United Nations' 2021 - 2016 goals for HIV prevention and viral load suppression. Interventions aimed at optimizing HIV prevention, testing and viral load suppression for adolescents must also attend to the intersectional realities influencing key sub-populations of SMM. Consequently, there is not a robust evidence-base to guide researchers and program partners on optimal approaches to implementing interventions with adolescent SMM. Using a multiple case study design, we integrated the Implementation Research Logic Model with components of the Consolidated Framework for Implementation Research and applied it as a framework for a comparative description of ten HIV related interventions implemented across five countries (Ghana, Kenya, Nigeria, Tanzania and United States). Using self-reported qualitative survey data of project principal investigators, we identified 17 of the most influential implementation determinants as well as a range of 17 strategies that were used in 90 instances to support intervention implementation. We highlight lessons learned in the implementation research process and provide recommendations for researchers considering future HIV implementation science studies with adolescent SMM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.072 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".