Application of Motor Development Scale: an integrative review
Bibliographic record
Abstract
ABSTRACT Objective: to know, understand, and analyze studies that employed the Motor Development Scale as a method for motor evaluation. Methods: the study included the databases Scielo, Pubmed, Lilacs, Science Direct, Web of Science, Scopus and Cochrane to identify the studies, using the following keywords: child; motor skills; motor skills disorders. The methodological quality of cross-sectional studies was analyzed by the Loney scale, cohort and case-control studies were assessed by the Newcastle-Ottawa scale, and clinical trials by the Physiotherapy Evidence Database. Results: twenty studies met the inclusion criteria. There was predominance of cross-sectional studies, which had as main outcome the analysis of motor development of schoolchildren, children with obesity and overweight, premature, with Attention Deficit Hyperactivity Disorder, learning disabilities and Down syndrome. The studies presented objective criteria to measure the outcome and for interpretation and applicability of adequate results, although they did not reach the minimum score established by the assessment scales. Conclusion: the Motor Development Scale is being used in Brazil in several contexts, presenting clear and statistically consistent results, although the methodologies of studies do not fully meet the standards of methodological quality.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".