Development and validation of an early childhood development scale for use in low-resourced settings
Bibliographic record
Abstract
BACKGROUND: Low-cost, cross-culturally comparable measures of the motor, cognitive, and socioemotional skills of children under 3 years remain scarce. In the present paper, we aim to develop a new caregiver-reported early childhood development (ECD) scale designed to be implemented as part of household surveys in low-resourced settings. METHODS: We evaluate the acceptability, test-retest reliability, internal consistency, and discriminant validity of the new ECD items, subscales, and full scale in a sample of 2481 18- to 36-month-old children from peri-urban and rural Tanzania. We also compare total and subscale scores with performance on the Bayley Scales of Infant Development (BSID-III) in a subsample of 1036 children. Qualitative interviews from 10 mothers and 10 field workers are used to inform quantitative data. RESULTS: Adequate levels of acceptability and internal consistency were found for the new scale and its motor, cognitive, and socioemotional subscales. Correlations between the new scale and the BSID-III were high (r > .50) for the motor and cognitive subscales, but low (r < .20) for the socioemotional subscale. The new scale discriminated between children's skills based on age, stunting status, caregiver-reported disability, and adult stimulation. Test-retest reliability scores were variable among a subset of items tested. CONCLUSIONS: Results of this study provide empirical support from a low-income country setting for the acceptability, reliability, and validity of a new caregiver-reported ECD scale. Additional research is needed to test these and other caregiver reported items in children in the full 0 to 3 year range across multiple cultural and linguistic settings.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".