An application of the ADAPT-ITT model to an evidence-based behavioral HIV prevention intervention for men who have sex with men in Ghana
Bibliographic record
Abstract
Despite constituting only about 1% of Ghana’s population, men who have sex with men (MSM) carry a disproportionate burden of HIV infections, constituting 18% of the population of people living with HIV in the country. Scholars have associated the disproportionate infection rates of HIV among MSM with existing structural factors (such as criminalization and stigma against MSM), and individual-level factors (such as sex without a condom, and transactional sex). Nonetheless, limited scholars consider intervention as an approach to reducing HIV and STI risk among MSM in the country. As such, in collaboration with community partners, we engaged MSM through the use of the ADAPT-ITT model to adapt the Many Men Many Voices (3MV) to address the needs of MSM. We addressed HIV/STD risk factors and ways to reduce HIV/STD infections. In this paper, we describe the use of the ADAPT-ITT model in the adoption and adaptation of the 3MV with MSM in Ghana. Whereas the 3MV was a good fit for our target population, we made modifications to fit the Ghanaian cultural setting by examining HIV and STD risk in the context of bisexuality, emphasizing on secrecy in location choice, and incorporating historical colonial setting in contextualizing sexuality and stigma in the Ghanaian sociocultural context. Our implementation process shows the efficacy of collaboration with community partners to implement culturally relevant interventions in HIV and STD prevention efforts in highly stigmatized environments.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".