Rating early child development outcome measurement tools for routine health programme use
Bibliographic record
Abstract
Background Identification of children at risk of developmental delay and/or impairment requires valid measurement of early child development (ECD). We systematically assess ECD measurement tools for accuracy and feasibility for use in routine services in low-income and middle-income countries (LMIC). Methods Building on World Bank and peer-reviewed literature reviews, we identified available ECD measurement tools for children aged 0–3 years used in ≥1 LMIC and matrixed these according to when (child age) and what (ECD domains) they measure at population or individual level. Tools measuring <2 years and covering ≥3 developmental domains, including cognition, were rated for accuracy and feasibility criteria using a rating approach derived from Grading of Recommendations, Assessment, Development and Evaluations. Results 61 tools were initially identified, 8% (n=5) population-level and 92% (n=56) individual-level screening or ability tests. Of these, 27 tools covering ≥3 domains beginning <2 years of age were selected for rating accuracy and feasibility. Recently developed population-level tools (n=2) rated highly overall, particularly in reliability, cultural adaptability, administration time and geographical uptake. Individual-level tool (n=25) ratings were variable, generally highest for reliability and lowest for accessibility, training, clinical relevance and geographical uptake. Conclusions and implications Although multiple measurement tools exist, few are designed for multidomain ECD measurement in young children, especially in LMIC. No available tools rated strongly across all accuracy and feasibility criteria with accessibility, training requirements, clinical relevance and geographical uptake being poor for most tools. Further research is recommended to explore this gap in fit-for-purpose tools to monitor ECD in routine LMIC health services.
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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 | 0.074 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".