Validity and reliability of <i>Meu Educativo</i>®: A new tool to assess fundamental movement skills in school‐aged children
Bibliographic record
Abstract
OBJECTIVES: There is evidence showing that children's proficiency in fundamental movement skills (FMS) is positively related to weight status, physical activity, fitness levels, cognitive development, as well as academic performance. Yet, the feasibility of FMS assessment is challenging, especially with relatively large samples of children. This article presents a new tool named Meu Educativo® to assess children's FMS levels, including its validity and reliability. METHODS: We relied on the validity evidence approach based on test content to develop the FMS assessment checklist and the rating system with a team of experts. In this article, we used five FMS (stationary dribbling, kick, overhand throw, catch the ball, and underhand roll). Further, we conducted a series of reliability studies and used percent agreement and Cohen's kappa (κ) as statistics. RESULTS: Experts agreed highly on the checklist's components in the five FMS. Inter-rater reliability ranged from 0.63 for stationary dribbling to 0.93 for overhand throw, and the intra-rater reliability ranged from 0.46 to 0.94. CONCLUSION: Based on the first results, Meu Educativo® was not only easy to use, but was also a reliable tool for FMS assessments. Notwithstanding the need for future studies, Meu Educativo® is a new tool that can be used by teachers, coaches, and motor development specialists dealing with school-aged children and researchers working with large samples.
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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".