Monitoring and evaluating the implementation of essential packages of health services
Bibliographic record
Abstract
Essential packages of health services (EPHS) are a critical tool for achieving universal health coverage, especially in low-income and lower middle-income countries. However, there is a lack of guidance and standards for monitoring and evaluation (M&E) of EPHS implementation. This paper is the final in a series of papers reviewing experiences using evidence from the Disease Control Priorities, third edition publications in EPHS reforms in seven countries. We assess current approaches to EPHS M&E, including case studies of M&E approaches in Ethiopia and Pakistan. We propose a step-by-step process for developing a national EPHS M&E framework. Such a framework would start with a theory of change that links to the specific health system reforms the EPHS is trying to accomplish, including explicit statements about the 'what' and 'for whom' of M&E efforts. Monitoring frameworks need to consider the additional demands that could be placed on weak and already overstretched data systems, and they must ensure that processes are put in place to act quickly on emergent implementation challenges. Evaluation frameworks could learn from the field of implementation science; for example, by adapting the Reach, Effectiveness, Adoption, Implementation and Maintenance framework to policy implementation. While each country will need to develop its own locally relevant M&E indicators, we encourage all countries to include a set of core indicators that are aligned with the Sustainable Development Goal 3 targets and indicators. Our paper concludes with a call to reprioritise M&E more generally and to use the EPHS process as an opportunity for strengthening national health information systems. We call for an international learning network on EPHS M&E to generate new evidence and exchange best practices.
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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 | 0.189 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".