Is It Possible to Reduce the Relative Age Effect through an Intervention on Motor Competence in Preschool Children?
Bibliographic record
Abstract
The purpose of the study was to find out whether a short 6-week intervention on motor competence can reduce the Relative Age Effect (RAE) of preschool children born in the first quarter, compared to those born in the fourth quarter of the same year. Seventy-six preschool children (5.20 ± 0.54 years) from Lugo (Spain) participated. A quasi-experimental pre-post-test design was used with an intervention group (n = 32) and a control group (n = 44). The Movement Assessment Battery for Children-2 (MABC-2) was used to collect data before and after the intervention. The data show that, before the intervention, there are significant differences between the control and the intervention group in favor of the former (born in the first quarter of the year) in manual dexterity (p = 0.011), balance (p = 0.002), total test score (p = 0.008), and total percentile score (p = 0.010). After the application of the specific intervention, statistically significant differences were found in aiming and catching (p < 0.001), balance (p = 0.022), total test score (p = 0.001), and total percentile score (p < 0.001) in favor of the intervention group (born in the last quarter of the year). The results obtained suggest that the application of a specific intervention on MC could positively influence the improvement of MC in preschool children (boys and girls) and reduce the differences produced by the RAE.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".