Enhancing Visual Perception in Children Ages 4-12 Years: A Systematic Review of Technology-based Interventions
Bibliographic record
Abstract
Introduction: Visual perception plays a pivotal role in a child’s overall development and learning. Occupational therapists often employ interventions to support children in enhancing their visual perception skills, with technology-based approaches gaining prominence in recent years. This review intends to highlight the significance of visual perception interventions, especially those involving technology. Aim: To systematically synthesise the literature on the effectiveness of technology-based interventions on visual perception in children with disabilities aged 4-12 years. Materials and Methods: A comprehensive search of studies was conducted using electronic databases (Scopus, PubMed, ProQuest, and OTseeker). Additionally, studies were also considered through manual searches from printed journals (American Journal of Occupational Therapy, British Journal of Occupational Therapy, Canadian Journal of Occupational Therapy, and the Australian Journal of Occupational Therapy) to identify existing technology-based visual perception interventions in children aged 4-12 years. Risk of Bias was conducted through guidelines for systematic review by the American Occupational Therapy Association (AOTA). Data extraction was reported by tabulating author(s) and year, sample characteristics, outcome measures used, study design, intervention details (experimental, comparator, study setting, duration), and outcomes of the studies. Results: In the present review of 13 studies, two studies used iPad interventions, while 11 used computer-based interventions, targeting various clinical groups like developmental delays, dyslexia, cerebral palsy, hearing impairment, down syndrome, hydrocephalus, and special needs. Occupational therapists led most studies, with some involving physiotherapists, educators, and multidisciplinary teams. iPad interventions focused on visual skills with structured apps, while computer methods included games and software like Microsoft Office and Computerised Visual Perception Training (CVPT) for visual training. Positive effects were seen on visual perception and motor skills across different conditions with these technology-based interventions. Conclusion: Visual perception interventions, particularly those incorporating technology, have become invaluable in the field of paediatric occupational therapy. As technology continues to evolve, occupational therapists must remain adaptive and innovative in their strategies to provide the best possible support for children with visual perception difficulties.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".