Bibliographic record
Abstract
Writing is a complex activity that requires the automation of graphomotor skills. Unfortunately, 10 to 30% of primary school students have difficulty at this level, which impairs the development of writing skills. It therefore seems judicious to intervene in kindergarten to support motor precision as well as visuomotor capacities, considered as prerequisites for writing by many researchers. The purpose of this study was to investigate the effect of motor training on visuomotor integration, motor precision and handwriting performance (speed and readability) in 5-year-old children. According to a quasi-experimental design (pretest, post-test with control group), 34 children participated in an intervention in subgroups, twice a week for 6 weeks. The mean and standard deviation were calculated for each of the tasks performed. Statistical tests (t test) were then carried out. The results show that the children in the experimental group improved their motor precision as well as their graphomotor skills compared to those in the control group. This project provides new insights into the benefits of working on basic skills in preparation for learning to write and will equip teachers on how to guide and support graphomotor skills before entering first grade.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".