Improving the Standards-Based Management-Recognition initiative to provide high-quality, equitable maternal health services in Malawi: an implementation research protocol
Bibliographic record
Abstract
BACKGROUND: The Government of Malawi is seeking evidence to improve implementation of its flagship quality of care improvement initiative-the Standards Based Management-Recognition for Reproductive Health (SBM-R(RH)). OBJECTIVE: This implementation study will assess the quality of maternal healthcare in facilities where the SBM-R(RH) initiative has been employed, identify factors that support or undermine effectiveness of the initiative and develop strategies to further enhance its operation. METHODS: Data will be collected in 4 interlinked modules using quantitative and qualitative research methods. Module 1 will develop the programme theory underlying the SBM-R(RH) initiative, using document review and in-depth interviews with policymakers and programme managers. Module 2 will quantitatively assess the quality and equity of maternal healthcare provided in facilities where the SBM-R(RH) initiative has been implemented, using the Malawi Integrated Performance Standards for Reproductive Health. Module 3 will conduct an organisational ethnography to explore the structures and processes through which SBM-R(RH) is currently operationalised. Barriers and facilitators will be identified. Module 4 will involve coordinated co-production of knowledge by researchers, policymakers and the public, to identify and test strategies to improve implementation of the initiative. POTENTIAL IMPACT: The research outcomes will provide empirical evidence of strategies that will enhance the facilitators and address the barriers to effective implementation of the initiative. It will also contribute to the theoretical advances in the emerging science of implementation research.
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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 | 0.140 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.047 | 0.008 |
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".