Application of individual-level and health system-level implementation science approaches to HIV and TB prevention in Uganda
Bibliographic record
Abstract
HIV and TB remain leading causes of death and disability across the globe. Highly efficacious HIV and TB prevention methods have created opportunities to prevent morbidity and mortality. Advancements in the availability of pre-exposure prophylaxis (PrEP) and post-exposure prophylaxis (PEP) have created opportunities to prevent HIV among those at high risk. Additionally, TB remains a leading cause of death among people with HIV. Isoniazid preventive therapy (IPT) reduces the incidence of active TB by approximately 40-60% but despite the breadth of evidence suggesting the benefits of IPT for people with HIV, uptake of IPT has been slow. Given the burden of TB and HIV globally, and the availability of medications to prevent HIV and treat latent TB, implementation science interventions provide an opportunity to do more to get these medications to those who need them most.In the second chapter of this dissertation, I evaluate the feasibility and preliminary effectiveness of integrating HIV prevention services into existing youth clubs in rural Uganda. As part of the intervention, we provided HIV prevention services, including access to PrEP and PEP. In addition, we taught multiple educational topics, including sexual and reproductive health, vocational training, and life skills, at these clubs over six months. In the third and fourth chapters of this dissertation, I focus on results from an implementation science intervention that was focused on the mid-level manager level of the health system in Uganda. These chapters used data from the SEARCH-IPT study in Uganda, which included a 3-year intervention among mid-level health managers with collaborative groups and leadership and management trainings to improve the uptake of TB preventive therapy for people with HIV. Overall, this body of work evaluates multiple aspects of individual-level and health system-level interventions to prevent HIV and TB infection in Uganda.
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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 | 0.032 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| 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".