Improving Retention in Care Among Pregnant Women and Mothers Living With HIV: Lessons From INSPIRE and Implications for Future WHO Guidance and Monitoring
Bibliographic record
Abstract
Identifying women living with HIV, initiating them on lifelong antiretroviral treatment (ART), and retaining them in care are among the important challenges facing this generation of health care managers and public health researchers. Implementation research attempts to solve a wide range of implementation problems by trying to understand and work within real-world conditions to find solutions that have a measureable impact on the outcomes of interest. Implementation research is distinct from clinical research in many ways yet demands similar standards of conceptual thinking and discipline to generate robust evidence that can be, to some extent, generalized to inform policy and service delivery. In 2011, the World Health Organization (WHO), with funding from Global Affairs Canada, began support to 6 implementation research projects in Malawi, Nigeria, and Zimbabwe. All focused on evaluating approaches for improving rates of retention in care among pregnant women and mothers living with HIV and ensuring their continuation of ART. This reflected the priority given by ministries of health, program implementers, and researchers in each country to the importance of women living with HIV returning to health facilities for routine care, adherence to ART, and improved health outcomes. Five of the studies were cluster randomized controlled trials, and 1 adopted a matched cohort design. Here, we summarize some of the main findings and key lessons learned. We also consider some of the broader implications, remaining knowledge gaps, and how implementation research is integral to, and essential for, global guideline development and to inform HIV/AIDS strategies.
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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 | 0.077 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".