Building coherent monitoring and evaluation plans with the Evaluation Planning Tool for global health
Bibliographic record
Abstract
Practitioners in global health are called to monitor and evaluate their projects. This keeps projects on track, it meets donor and public demand, and it is a key mechanism by which global health organizations hold themselves accountable and improve their community of practice. However, monitoring and evaluation (M&E) is time- and resource-consuming, bringing into question whether the effort expended on M&E is worth it. While there has been a shift towards emphasizing the learning aspect of M&E, non-governmental organizations (NGOs) and other actors still struggle to get value from their efforts. One reason for this is that M&E plans are often not coherent or employed to their full potential. Theories of change, indicator lists, and data collection become a series of disjointed efforts that do not tie together. They become tick-the-box exercises to satisfy donors rather than a logical approach to draw meaningful findings for stakeholders, governments, and local communities. In this paper, we propose a step-by-step approach to utilizing M&E tools to their fullest potential, including: (1) a clearly defined theory of change that captures all program pathways and shows all intermediate objectives needed to achieve impact, (2) indicators which directly reflect the intermediate and ultimate objectives in the theory of change, and (3) a data collection plan which includes appropriate methods to measure indicators and address the questions stakeholders want answered. We make the case for a simpler, more coherent approach to M&E and propose a new tool to help practitioners more easily develop evaluation plans that are rigorous, practical, and worth the effort.
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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 | 0.144 | 0.175 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".