Creation of the WHO Indicators of Infant and Young Child Development (IYCD): metadata synthesis across 10 countries
Bibliographic record
Abstract
BACKGROUND: Renewed global commitment to the improvement of early child development outcomes, as evidenced by the focus of the United Nations Sustainable Development Goal 4, highlights an increased need for reliable and valid measures to evaluate preventive and interventional efforts designed to affect change. Our objective was to create a new tool, applicable across multicultures, to measure development from 0 to 3 years through metadata synthesis. METHODS: Fourteen cross-sectional data sets were contributed on 21 083 children from 10 low/middle-income countries (LMIC), assessed using seven different tools (caregiver reported or directly assessed). Item groups, measuring similar developmental skills, were identified by item mapping across tools. Logistic regression curves displayed developmental trajectories for item groups across countries and age. Following expert consensus to identify well-performing items across developmental domains, a second mapping exercise was conducted to fill any gaps across the age range. The first version of the tool was constructed. Item response analysis validated our approach by putting all data sets onto a common scale. RESULTS: 789 individual items were identified across tools in the first mapping and 129 item groups selected for analysis. 70 item groups were then selected through consensus, based on statistical performance and perceived importance, with a further 50 items identified at second mapping. A tool comprising 120 items (23 fine motor, 23 gross motor, 20 receptive language, 24 expressive language, 30 socioemotional) was created. The linked data sets on a common scale showed a curvilinear trajectory of child development, highlighting the validity of our approach through excellent coverage by age and consistency of measurement across contributed tools, a novel finding in itself. CONCLUSIONS: We have created the first version of a prototype tool for measuring children in the early years, developed using novel easy to apply methodology; now it needs to be feasibility tested and piloted across several LMICs.
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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 | 0.057 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.039 | 0.038 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".