Association between reduced visual-motor integration performance and socioeconomic factors among preschool children in Malaysia: A cross-sectional study
Bibliographic record
Abstract
Visual-motor integration (VMI) is related to children's academic performance and school readiness. VMI scores measured using the Beery-Bucktenicka Developmental Test of Visual-Motor Integration (Beery-VMI) can differ due to differences in cultural and socioeconomic backgrounds. This study compared the VMI scores of Malaysian preschoolers with the corresponding US norms and determined the association between their VMI scores and socioeconomic factors. A cross-sectional study was conducted among 435 preschoolers (mean age: 5.95±0.47 years; age range: 5.08-6.83 years) from randomly selected public and private preschools. VMI scores were measured using Beery-VMI in the preschools' classrooms. Information on the socioeconomic characteristics of the preschoolers was obtained using a parent-report questionnaire. One sample t-test was used to compare their VMI scores with the corresponding US norms. Multivariate logistic regression models were used to explore the influence of socioeconomic factors on the preschoolers' VMI scores. Overall, Malaysian preschoolers' VMI performance was similar to the US standardized norms (p>0.05). Children from low-income families were twice likely to obtain lower than average VMI scores than those from higher-income families (OR = 2.47, 95%CI 1.05, 5.86). Children enrolled at public preschools were more likely to obtain a lower than average VMI score than those who enrolled at private preschools (OR = 2.60, 95%CI 1.12, 6.06). Children who started preschool at the age of six were more likely to obtain lower than average VMI scores than those who started at an earlier age (OR = 4.66, 95%CI 1.97, 11.04). Low maternal education level was also associated with lower than average VMI score (OR = 2.60, 95%CI 1.12, 6.06). Malaysian preschoolers' Beery-VMI performance compared well to their US counterparts. Some socioeconomic factors were associated with reduced VMI scores. Those from disadvantaged socioeconomic backgrounds are more likely to have reduced VMI performance, potentially adversely affecting their school readiness, cognitive performance, and future academic achievements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".