Adaptation of the Mullen Scales of Early Learning for use among infants aged 5‐ to 24‐months in rural Gambia
Bibliographic record
Abstract
Infants in low-resource settings are at heightened risk for compromised cognitive development due to a multitude of environmental insults in their surroundings. However, the onset of adverse outcomes and trajectory of cognitive development in these settings is not well understood. The aims of the present study were to adapt the Mullen Scales of Early Learning (MSEL) for use with infants in a rural area of The Gambia, to examine cognitive development in the first 24-months of life and to assess the association between cognitive performance and physical growth. In Phase 1 of this study, the adapted MSEL was tested on 52 infants aged 9- to 24-months (some of whom were tested longitudinally at two time points). Further optimization and training were undertaken and Phase 2 of the study was conducted, where the original measures were administered to 119 newly recruited infants aged 5- to 24-months. Infant length, weight and head circumference were measured concurrently in both phases. Participants from both phases were split into age categories of 5-9 m (N = 32), 10-14 m (N = 92), 15-19 m (N = 53) and 20-24 m (N = 43) and performance was compared across age groups. From the ages of 10-14 m, Gambian infants obtained lower MSEL scores than US norms. Performance decreased with age and was lowest in the 20-24 m old group. Differential onsets of reduced performance were observed in the individual MSEL domains, with declines in visual perception and motor performance detected as early as at 10-14 months, while reduced language scores became evident after 15-19 months of age. Performance on the MSEL was significantly associated with measures of growth.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".