Evaluation of child motor development and its association with social vulnerability
Bibliographic record
Abstract
ABSTRACT This study aimed to evaluate the motor development of children aged four to 17 months and investigate its association with sociodemographic risk factors. This is a cross-sectional descriptive study conducted with clinically stable children aged four to 17 months from the pediatric inpatient unit of a public hospital in Porto Alegre, RS, and whose hospital discharge would happen soon. For the evaluation of sociodemographic risk factors, a questionnaire developed by the researchers was used which addressed biological, social and environmental factors. The Alberta Infant Motor Scale (AIMS), in its version translated, adapted and validated to Brazilian Portuguese, was used in the evaluation of motor development. In statistical analysis, Student’s t-test and Chi-square test were used with significance level of 5% (p≤0.05) for all tests. From a total of 110 evaluated children, motor performance was lower than expected in more than half of them (63.6%, n=70). Motor development presented statistically significant associations with delayed vaccines (p=0.005), cohabitation with smokers (p=0.047), and receiving socioeconomic benefits (p=0.036). In conclusion, social factors such as delayed vaccines, cohabitation with smokers and receiving socioeconomic benefits may be associated with risk factors related to motor development of children aged four months to 17 months old.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.010 | 0.002 |
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".