Examining Factors of Accelerometer-Measured Sedentary Time in a Sample of Rural Canadian Children
Bibliographic record
Abstract
The aim of this study was to examine potential child-level and day-level factors of accelerometer-measured sedentary time in a sample of rural Canadian children. Children (n = 86) from rural Northwestern Ontario participated in this study. Children’s sedentary times were identified and logged using an accelerometer. Child-level data (socio-demographic, household, and environment) came from surveys of children and their parents and a passively logging global positioning unit. Day-level data on day type (weekday/weekend) and weather (temperature, precipitation) were based on the dates of data collection and meteorological data came from the closest Environment Canada weather station. Cross-classified regression models were used to assess the relationship between child-level and day-level correlates of sedentary time. Boys were less sedentary than girls (b = −30.53 p = 0.01). For each one-year age increase, children’s sedentary time increased (b = 12.79 p < 0.01). This study indicates a difference in sedentary time based on a child’s age and gender. However, family, environmental, and weather characteristics did not influence sedentary time in this sample. Health practitioners who deliver care for northern rural youth can provide targeted health advice regarding sedentary time and consider gender and age to be risk factors for these behaviors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".