Counting outcomes, coverage and quality for early child development programmes
Bibliographic record
Abstract
Improved measurement in early child development (ECD) is a strategic focus of the WHO, UNICEF and World Bank Nurturing Care Framework. However, evidence-based approaches to monitoring and evaluation (M&E) of ECD projects in low-income and middle-income countries (LMIC) are lacking. The Grand Challenges Canada®-funded Saving Brains® ECD portfolio provides a unique opportunity to explore approaches to M&E of ECD programmes across diverse settings. Focused literature review and participatory mixed-method evaluation of the Saving Brains portfolio was undertaken using an adapted impact framework. Findings related to measurement of quality, coverage and outcomes for scaling ECD were considered. Thirty-nine ECD projects implemented in 23 LMIC were evaluated. Projects used a 'theory of change' based M&E approach to measure a range of inputs, outputs and outcomes. Over 29 projects measured cognitive, language, motor and socioemotional outcomes. 18 projects used developmental screening tools to measure outcomes, with a trade-off between feasibility and preferred practice. Environmental inputs such as the home environment were measured in 15 projects. Qualitative data reflected the importance of measurement of project quality and coverage, despite challenges measuring these constructs across contexts. Improved measurement of intervention quality and measurement of coverage, which requires definition of the numerator (ie, intervention) and denominator (ie, population in need/at risk), are needed for scaling ECD programmes. Innovation in outcome measurement, including intermediary outcome measures that are feasible and practical to measure in routine services, is also required, with disaggregation to better target interventions to those most in need and ensure that no child is left behind.
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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 | 0.072 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".