Stability of the Bayley II Scales of Infant Development in a sample of low-risk and high-risk infants
Bibliographic record
Abstract
The aim of this study was to examine the stability of the second edition of the Bayley Scales of Infant Development (Bayley II) by correlating Bayley II scores during the first year of life with Bayley II scores at approximately 18 months of age. Bayley II Mental and Motor Scales were administered to 119 infants (69 males, 50 females) during their first year of life; mean age for low-risk group 7 months (SD 3.01) and mean age for high-risk group 7.8 months (SD 2.46). A second set of Bayley II scores was collected on 104 of the infants at 17 to 22 months of age; mean age for low-risk group 18.5 months (SD 1.07) and mean age for high-risk group 18.9 months (SD 1.11). Mean time interval between the two assessments was 11.5 months for the low-risk group and 11.15 months for the high-risk group. Stability of the scores over time was assessed. Correlations for the Bayley II Mental Developmental Index and Psychomotor Developmental Index between administrations during the first and second years of life were r=0.49 (p<0.001) and r=0.48 (p<0.001) respectively. We conclude that approximately 23 to 24% of the variance in the infants' later Bayley II scores could be explained by the earlier scores. This is one of only two studies to examine the stability of Bayley II over time. Replication studies are needed to evaluate the consistency of these findings across other samples of infants.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".