Participatory Training in Monitoring and Evaluation for Maternal and Newborn Health Programmes
Bibliographic record
Abstract
In the context of slow progress towards Millennium Development Goals for child and maternal health, an innovative participatory training programme in the monitoring and evaluation (M&E) of Maternal and Newborn Health programmes was developed and delivered in six developing countries. The training, for health professionals and programme managers, aimed: (i) to strengthen participants' skills in M&E to enable more effective targeting of resources, and (ii) to build the capacity of partner institutions hosting the training to run similar courses. This review aims to assess the extent to which these goals were met and elicit views on ways to improve the training. An online survey of training participants and structured interviews with stakeholders were undertaken. Data from course reports were also incorporated. There was clearly a benefit to participants in terms of improved knowledge and skills. There is also some evidence that this translated into action through M&E implementation and tool development. Evidence of capacity-building at an institutional level was limited. Lessons for professional development training can be drawn from several aspects of the training programme that were found to facilitate learning, engagement and application. These include structuring courses around participant material, focussing on the development of practical action plans and involving multi-disciplinary teams. The need for strengthening follow-up and embedding it throughout the training was highlighted to overcome the challenges to applying learning in the 'real world'.
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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 | 0.315 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| 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".