Prioritizing the PMTCT Implementation Research Agenda in 3 African Countries
Bibliographic record
Abstract
Countries with high HIV prevalence face the challenge of achieving high coverage of antiretroviral drug regimens interventions including for the prevention of mother-to-child transmission of HIV (PMTCT). In 2011, the World Health Organization and the Department of Foreign Affairs, Trade and Development, Canada, launched a joint implementation research (IR) initiative to increase access to effective PMTCT interventions. Here, we describe the process used for prioritizing PMTCT IR questions in Malawi, Nigeria, and Zimbabwe. Policy makers, district health workers, academics, implementing partners, and persons living with HIV were invited to 2-day workshops in each country. Between 42 and 70 representatives attended each workshop. Using the Child Health Nutrition Research Initiative process, stakeholder groups systematically identified programmatic barriers and formulated IR questions that addressed these challenges. IR questions were scored by individual participants according to 6 criteria: (1) answerable by research, (2) likely to reduce pediatric HIV infections, (3) addresses main barriers to scaling-up, (4) innovation and originality, (5) improves equity among underserved populations, and (6) likely value to policy makers. Highest scoring IR questions included health system approaches for integrating and decentralization services, ways of improving retention-in-care, bridging gaps between health facilities and communities, and increasing male partner involvement. The prioritized questions reflect the diversity of health care settings, competing health challenges and local and national context. The differing perspectives of policy makers, researchers, and implementers illustrate the value of inclusive research partnerships. The participatory Child Health Nutrition Research Initiative approach effectively set national PMTCT IR priorities, promoted country ownership, and strategically allocated research resources.
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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 | 0.089 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| 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".