Virtual reality-based fine motor skills training in paediatric rehabilitation: a protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Fine motor skill (FMS) development during childhood is essential to many learning processes, especially in school. FMS impairment can have a major impact on children's quality of life. Developing effective and engaging rehabilitation solutions to train FMS that engage children in the abundant practice required for motor learning can be challenging. Virtual reality (VR) is a promising intervention option offering engaging FMS training tasks and environments that align with evidence-based motor learning principles. Other potential advantages of VR for rehabilitation include accessibility for home-based use and adaptability to individual needs. The objective of this scoping review is to map the extent, range and nature of VR applications focused on FMS training in paediatric rehabilitation, including hardware, software and interventional parameters. METHODS AND ANALYSIS: We are following methodological guidelines for scoping review conduct and reporting from the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews recommendations. We will search four databases (Pubmed, Web of Science, PsycInfo and Scopus) for articles that meet inclusion criteria defined by the Population, Concept, Context method; specifically studies focused on development or evaluation of immersive or non-immersive VR applications to deliver FMS training in paediatric rehabilitation. Different populations of children with FMS impairments will be included (such as children with cerebral palsy, children with developmental coordination disorder or attention deficit hyperactivity disorder). The first search took place in December 2023, and a second is planned for February 2025. One reviewer will complete title, abstract and full paper screening, with consultation by a second reviewer in case of uncertainty. A data extraction framework will be tested by two reviewers on five randomly selected studies to ensure inter-rater reliability, and one reviewer will complete data extraction. Quantitative and qualitative extraction will follow JBI guideline recommendations. Results will be presented in a descriptive and tabular format, including a narrative summary. Results will enhance understanding of the potential of FMS training in VR and inform subsequent directions for research and clinical practice. ETHICS AND DISSEMINATION: Data for this review will be collected from the published literature. Ethical approval is not required. We will present our findings at scientific conferences and submit this review to a peer-reviewed journal for publication.
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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 | 0.071 | 0.064 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.073 | 0.015 |
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".