A discriminant function analysis of high and low active children as measured by pedometers
Bibliographic record
Abstract
A large proportion of Canadian children fail to acquire recommended levels of physical activity per day. Therefore it is important for researchers to understand the factors that determine whether children are active or not. Purpose: To explore sociodemographic variables that discriminate between high and low active children as measured by pedometers. Methods: Between April 2009 and February 2011, 421 children aged 6 to 10 years-old and one of their parents wore SC-T2 pedometers for four consecutive days. High and low activity levels of the children were determined via a median split. Children's height and weight were directly measured, and parent's height, weight and demographic information were self-reported. The dependent variables included: child age and sex, parent and child body mass index, average parent steps as well as parent education, household income and season. A discriminant function analysis was used to determine whether the above mentioned variables contributed to group separation of high and low active children. Results: Wilks' Lambda was significant, ?2(7) = 37.15, p < .001, with parent steps, season, and sex contributing to group separation. Conclusion: These findings suggest that parental modeling of physical activity, season and sex are key factors in determining whether children are active or not and thus should be taken into consideration when developing interventions and public health initiatives. Acknowledgments: This research was funded by the Heart & Stroke Foundation of Canada and the Canadian Institute of Health Research (CIHR)
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".