Relationships Between Fine-Motor, Visual-Motor, and Visual Perception Scores and Handwriting Legibility and Speed
Bibliographic record
Abstract
Occupational therapists assess fine motor, visual motor, visual perception, and visual skill development, but knowledge of the relationships between scores on sensorimotor performance measures and handwriting legibility and speed is limited. Ninety-nine students in grades three to six with learning and/or behavior problems completed the Upper-Limb Speed and Dexterity Subtest of the Bruininks-Oseretsky Test of Motor Proficiency, the Beery-Buktenica Developmental Test of Visual-Motor Integration-5th Edition, the Test of Visual Perceptual Skills-Revised, the Visual Skills Appraisal, and a handwriting copying task. Correlations between sensorimotor performance scores and handwriting legibility varied from .07 to .38. Correlations between sensorimotor performance scores and handwriting speed varied from .04 to .42. Stepwise multiple regression analysis indicated that the variance in handwriting explained by these measures was ≤ 20% for legibility and ≤ 26% for speed. On the basis of multivariate analysis of variance only scores for the Developmental Test of Visual-Motor Integration differed between students classified as "skilled" and "unskilled" handwriters. The low magnitude of the correlations and variance explained by the sensorimotor performance measures supports the need for occupational therapists to consider additional factors that may impact handwriting of students with learning and/or behavior problems.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".