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 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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".