[Evaluation of visual-motor integration functions in children between 6-15 years of age].
Bibliographic record
Abstract
OBJECTIVE: Visual-motor integration skills are considered an essential domain of clinical and psycho-educational assessment. The goal of the present investigation is to provide the Turkish norms for the Beery-Buktenica Developmental Visual-Motor Integration Test (VMI-4th) for children and adolescents between the ages of 6-15 years as part of a comprehensive neuropsychological test battery. METHOD: A total of 1887 children from elementary and high schools in the city of Bursa were recruited for this study. From this sample 44 children were re-tested 3-4 weeks following the first administration for test-retest reliability. RESULTS: Findings showed clear developmental trajectories in visual-motor integration skills. Significant performance increments were observed in six month intervals for ages 6 and 7. Starting from age 8, norms were established for each age group separately. Girls and boys performed similarly on the VMI-4. Test- retest correlation was modest but within an acceptable range. CONCLUSION: The age-based norms established for the VMI-4 in this study can be used to assess children between the ages of 6-15 years as part of a clinical neuropsychological and a psycho-educational assessment. The mean VMI scores presented in this study represent performance of children in middle and middle-upper socio-economic status and may not represent the normal performance range of children from lower SES.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".