Intra-Individual Variability in Gross Motor Development in Healthy Full-Term Infants Aged 0–13 Months and Associated Factors during Child Rearing
Bibliographic record
Abstract
The gross motor development of a typically developing infant is a dynamic process, the intra-individual variability of which can be investigated through longitudinal assessments. Changes in gross motor development vary, according to the interaction of multiple sub-systems within the child, environment, task setting, and experience or practice of movement. At present, studies on environmental factors that influence gross motor development in full-term infants over time are limited. The main aim of this study was to investigate environmental factors affecting intra-individual variability from birth to 13 months. The gross motor development of 41 full-term infants was longitudinally assessed every month from the age of 15 days using the Alberta Infant Motor Scale. Parents were interviewed monthly about environmental factors during childcare. Infants showed fluctuations in the percentile of gross motor development, and no systematic pattern was detected. The total mean range of gross motor percentile was 65.95 (SD = 15.74; SEM = 2.28). The percentiles of gross motor skills over the 14 assessments ranged from 36 to 93 percentile points. Factors that were significantly associated with the gross motor development percentile were the use of a baby walker (Coef. = −8.83, p ≤ 0.0001) and a baby hammock (Coef. = 7.33, p = 0.04). The use of baby hammocks could increase the gross motor percentile by 7.33 points. Although the usage of a baby walker is common practice in childcare, it may cause a decrease in the gross motor percentile by 8.83 points according to this study. In conclusion, healthy full-term infants exhibited a natural variability in gross motor development. Placing infants in a baby walker during the first year of age should be approached with caution due to the risk of delayed gross motor development.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".