Effectiveness of iPad apps on visual-motor skills among children with special needs between 4y0m–7y11m
Bibliographic record
Abstract
AIMS: The aim of this randomized controlled trial was to assess the effectiveness of interventions using iPad applications compared to traditional occupational therapy on visual-motor integration (VMI) in school-aged children with poor VMI skills. METHODS: Twenty children aged 4y0m to 7y11m with poor VMI skills were randomly assigned to the experimental group (interventions using iPad apps targeting VMI skills) or control group (traditional occupational therapy intervention sessions targeting VMI skills). The intervention phase consisted of two 40-min sessions per week, over a period of 10 weeks. Participants were required to attend a minimum of 8 and a maximum of 12 sessions. The subjects were tested using the Beery-VMI and the visual-motor subscale of the M-FUN, at baseline and follow-up. RESULTS: Results from a 2-way mixed design ANOVA yielded significant results for the main effect of time for the M-FUN total raw score, as well as in the subscales Amazing Mazes, Hidden Forks, Go Fishing and VM Behavior. However, gains did not differ between intervention types over time. No significant results were found for the Beery-VMI. CONCLUSIONS: This study supports the need for further research into the use of iPads for the development of VMI skills in the pediatric population. Implications for Rehabilitation This is the first study to look at the use of iPads with school-aged children with poor visual-motor skills. There is limited literature related to the use of iPads in pediatric occupational therapy, while they are increasingly being used in practice. When compared to the traditional occupational therapy interventions, participants in the iPad intervention appeared to be more interested, engaged and motivated to participate in the therapy sessions. Using iPad apps as an adjunct to therapy in intervention could be effective in improving VMI skills over time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".