Computing and Evaluating Relationships Between Equal and Differential Factor Weighting for Fundamental Movement Skills and Physical Activity with Guided Active Play During Childhood
Bibliographic record
Abstract
Background/Objectives: The Test of Gross Motor Development (TGMD-2) totals assume equal weighting of the 12 locomotor (LOC) and object control (OC) skills, yet validation studies indicate differential contributions. The study compared equal- and differential-weighted scores for LOC and OC skills, with three fitness and two physical activity (PA) outputs during guided active play (GAP). Methods: Children’s (n = 82; 7.6 ± 1.5 years) TGMD-2 LOC and OC differential factor weights were estimated with Exploratory Factor Analysis (EFA) and compared to equal weights with multiple linear regression (two, five, and eight predictors) and Chi-square analyses. Predictor variables included fitness, BMI, sex, age stages, and PA assessed by energy expenditure (PAEE) and intensity (MVPA) estimated using accelerometry during 1 h GAP. Results: EFA supported a two-factor structure (variance explained = 51.1%) with ≥0.500 loadings for 9/12 skills. Differential- and equal-weighted LOC and OC scores showed varied contributions from individual skills. Multiple linear regression analysis showed similar explained variances (R2) of 53% (PAEE), 40% (MVPA), 31% (OC), and 14% (LOC) for equal or differential scores with eight predictors. Although β coefficients varied, going from two, five, and eight predictors, the impact of equal and differential weights was comparable. Chi-square analysis indicated high OC associated with MVPA (X2 (4) = 9.42, p ≤ 0.05), LP, and STR with PAEE. Conclusions: TGMD-2 outputs with equal- and differential-weighted scores are adequate for clinical/educational use, which show similar relationships with PA and HRF variables. Differential-weighted TGMD-2 scores comprise different contributions of movement skills and may hold promise for intervention studies focused on varied or target tasks and movement abilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".