Referee report. For: Using developmental evaluation to implement an oral pre-exposure prophylaxis (PrEP) project in Kenya [version 1; peer review: 1 approved with reservations]
Bibliographic record
Abstract
Oral Pre-Exposure Prophylaxis (PrEP) is highly effective in lowering HIV transmission risk. The Bill and Melinda Gates-funded Jilinde Project was designed to identify the best ways to introduce and support PrEP services in Kenya for female sex workers, men who have sex with men, and adolescent girls and young women. We chose Developmental Evaluation (DE) as a core project approach because our goal was not just to recruit 20,000 new PrEP users, but to learn how to deliver PrEP effectively to optimally benefit users in a complex, dynamic, resource-limited setting. This paper describes how we incorporated DE into the Jilinde Project, and shares experiences and lessons learned about the value of DE in PrEP service implementation in a real-world situation. With the Ministry of Health, Jilinde developed consensus about the structure and roll-out of PrEP services. The DE evaluator, embedded in Jilinde, designed and implemented the five-step DE methodology—collect, review, reflect, record and act—according to a core set of project guiding principles. The paper describes how we operationalized the five elements, citing findings reported and actions taken reflecting on the data. It summarizes challenges to DE implementation, such as uneven uptake and competing demands, and how we addressed those challenges. Used consistently, DE helped adapt and refine PrEP services, improve service access, reach target audiences and improve continuation rates. The look, feel and yield of our DE efforts evolved over time, increasingly integrated into existing systems and providing deeper and richer understandings, and we learned how to better implement DE in the future. This case study provides practical guidance for using a DE approach in program design. The DE process can be used successfully working with partners on a common complex public health challenge within a dynamic environment in a way that feeds back into and improves programs.
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 | 0.014 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.310 | 0.116 |
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".