Beery VMI Scores of Children with Autism Spectrum Disorder Undergoing Occupational Therapy
Bibliographic record
Abstract
Visual motor integration (VMI) is the ability to control hand movements through vision. Children with autism are at risk for VMI deficits although this correlation is well described in previous research not much else is known about the relationship between autism and VMI. This study investigated the potential predictors of VMI performance in children with autism. The impact of occupational therapy attendance, age, gender, and pre-Beery-Buktenica Developmental Test of VMI scores on post-Beery-Buktenica Developmental Test of VMI scores were analyzed. Secondary data from 104 subjects were analyzed using multiple linear regression. It was concluded that the pre-Beery-Buktenica Developmental Test of VMI score was the greatest predictor of the post-Beery-Buktenica Developmental Test of VMI score. Age and gender were not predictive. Occupational therapy attendance was not a significant predictor; however, there was a significant difference between pre and post-assessment scores. The findings of this study illustrate that children with autism who have VMI deficits can benefit from rehabilitation services, that all ages and both genders can expect similar positive outcomes, and that these positive changes were not limited by attendance. Professionals can utilize the predictive model to formulate realistic goals based on current VMI performance for both rehabilitative and school settings.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".