Building Health System Capacity Through Implementation Research: Experience of INSPIRE—A Multi-country PMTCT Implementation Research Project
Bibliographic record
Abstract
BACKGROUND: The INSPIRE-Integrating and Scaling Up PMTCT through Implementation REsearch-initiative was established as a model partnership of national prevention of mother-to-child transmission of HIV (PMTCT) implementation research in 3 high HIV burden countries-Malawi, Nigeria, and Zimbabwe. INSPIRE aimed to link local research groups with Ministries of Health (MOH), build local research capacity, and demonstrate that implementation research may contribute to improving health care delivery and respond to program challenges. METHODOLOGY: We used a mixed methods approach to review capacity building activities, as experienced by health care workers, researchers, and trainers conducted in the 6 INSPIRE projects before and during study implementation. RESULTS: Between 2011 and 2016, over 3400 health care workers, research team members, and community members participated in INSPIRE activities. This included research prioritization exercises, proposal development, good clinical practice and research ethics training, data management and analysis workshops, and manuscript development. Health care workers in clinics and district health offices acknowledged the value of hosting implementation research projects and how the quality of services improved. Research teams acknowledged the opportunities that projects provided for personal development and the value of participating in a multicountry research network. DISCUSSION: INSPIRE provided an opportunity for African-led research in which researchers worked closely with national MOH to identify priority research questions and implement studies. Close partnerships between research teams and local implementers facilitated project responsiveness to local program issues. Consequently, processes and training needed for study implementation also improved local program management and service delivery. Additional benefits included improved data management, publications, and career development.
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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 | 0.133 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".