Socioeconomic and gender-based disparities in the motor competence of school-age children
Bibliographic record
Abstract
This study examined socioeconomic and gender-based disparities in motor competence (MC) amongst 6-12-year-old children (N = 2654). Validated product-oriented tests assessing agility, balance and coordination were used to measure MC. School-level socioeconomic status (low, middle, high) was used to assess socioeconomic disparities. Analysis of covariance (ANCOVA) were conducted and odds ratios were calculated for the likelihood of having low MC by gender and socioeconomic status (SES). Girls displayed lower MC than boys for agility and coordination involving object-control (P < 0.001) while boys scored lower than girls for balance and hand-foot coordination (P < 0.001). Children in high SES schools displayed the highest level of MC for agility, balance and coordination (P < 0.001). Compared to the children in high SES schools, odds of having low competence in balance was higher for the children in low SES schools and odds of having low competence in agility and coordination were higher for the children in both low and middle SES schools. Newell’s model of constraints (1986) and Bourdieu’s concept of habitus (1984) were used to consider potential explanations of the observed disparities. To level up inequalities in children’s MC, resources invested in school-based interventions should be proportionate to the school SES.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".