The relationship between girls’ (8–14 years) physical activity and maternal education
Bibliographic record
Abstract
BACKGROUND: Mothers with higher levels of education are more likely to engage in health-promoting behaviour; thus, it is intuitive that a child's physical activity would be positively related to maternal education. The literature on this area, however, is inconclusive and may be due to the methods used to assess physical activity (i.e. the use of aggregated and self-reported physical activity that may not reflect the true and detailed variation of physical activity). AIM: To profile the physical activity behaviours of girls with mothers of differing educational attainment. SUBJECTS AND METHODS: 77 girls (grades 4-8) wore an Actical accelerometer for 7 days. Minutes spent sedentary and in light, moderate and vigorous physical activity per day over 7 days, 5 weekdays, 2 weekends, and 1 h commuting period to and from school of girls of University educated (UE) and non-UE mothers were analysed. RESULTS: After controlling for confounders, girls with UE mothers were more likely to participate in vigorous physical activity at the weekend and moderate-to-vigorous physical activity during the morning commute to school. CONCLUSIONS: Research investigating the relationship between maternal education and child's physical activity should explore, at higher resolution, when activity is accumulated, in particular separating weekday and weekend physical activity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".