Fine Motor Activities Program to Promote Fine Motor Skills in a Case Study of Down’s Syndrome
Bibliographic record
Abstract
Children with Down’s syndrome have developmental delays, particularly regarding cognitive and motor development. Fine motor skill problems are related to motor development. They have impact on occupational performances in school-age children with Down’s syndrome because they relate to participation in school activities, such as grasping, writing, and carrying out self-care duties. This study aimed to develop a fine motor activities program and to examine the efficiency of the program that promoted fine motor skills in a case study of Down’s syndrome. The case study subject was an 8 -year-old male called Kai, who had Down’s syndrome. He was a first grader in a regular school that provided classrooms for students with special needs. This study used the fine motor activities program with assessment tools, which included 3 subtests of the Bruininks-Oseretsky Test of Motor Proficiency, second edition (BOT-2) that applied to Upper-limb coordination, Fine motor precision and Manual dexterity; as well as the In-hand Manipulation Checklist, and Jamar Hand Dynamometer Grip Test. The fine motor activities program was implemented separately and consisted of 3 sessions of 45 activities per week for 5 weeks, with each session taking 45 minutes. The results showed obvious improvement of fine motor skills, including bilateral hand coordination, hand prehension, manual dexterity, in-hand manipulation, and hand muscle strength. This positive result was an example of a fine motor intervention program designed and developed for therapists and related service providers in choosing activities that enhance fine motor skills in children with Down’s syndrome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".