A multivariate multilevel approach to unravel the associations between individual and school factors on children's motor performance in the <scp>REACT</scp> project
Bibliographic record
Abstract
OBJECTIVE: The aim was to (1) estimate the relationship between physical fitness (PF) and object control fundamental movement skills (FMS), (2) identify child characteristics that relate with PF and FMS, and (3) examine associations between the school environment, PF, and FMS. METHODS: The sample included 1014 Portuguese children aged 6-10 years from the REACT project. PF was assessed via running speed, shuttle run, standing long jump, handgrip, and the PACER test. Object control FMS were assessed with stationary dribble, kick, catch, overhand throw, and underhand roll. Test performances were transformed into z-scores, and their sum was expressed as overall PF and FMS. Child-level variables included body mass index (BMI) z-scores, accelerometer-measured sedentary time and moderate-to-vigorous physical activity, and socioeconomic status (SES). School size, physical education classes, practice areas, and equipment were also assessed. RESULTS: Approximately, 90% of the variance in object control PF and FMS was at the child level, and 10% at the school level. The correlation between PF and object control FMS was .62, which declined to .43 with the inclusion of covariates. Older, more active, and higher SES children had higher object control PF and FMS, and boys outperformed girls. BMI was negatively associated with PF but not with object control FMS. Sedentary time and number of physical education classes were not significant predictors. Most school predictors did not jointly associate with PF and object control FMS. CONCLUSION: PF and object control FMS z-scores were moderately related. Not all child characteristics were associated with both PF and object control FMS, and their effect sizes were different. School characteristics only explained 10% of the total variation in PF and object control FMS.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".