Identifying risk profiles for excess sedentary behaviour in youth using individual, family and neighbourhood characteristics
Bibliographic record
Abstract
= 294) both at 8-10 and 10-12 years of age (Visits 1 and 2, respectively), using data from the QUebec Adipose and Lifestyle InvesTigation in Youth. Excess SB was measured by accelerometry and defined as >50% of total wear time at <100 counts/minutes. Recursive partitioning analyses were performed with candidate individual-, family-, and neighbourhood-level factors assessed at Visit 1, and distinct groups at varying risk of excess SB were identified for both timepoints. From the ages of 8-10 to 10-12 years, the prevalence of excess SB more than doubled (24.5% to 57.1%). At Visit 1, excess SB was greatest (73%) among children simultaneously not meeting physical activity guidelines, reporting >2 h/day of weekday non-academic screen time, living in low-dwelling density neighbourhoods, having poor park access, and living in neighbourhoods with greater disadvantage. At Visit 2, the high-risk group (70%) was described by children simultaneously not meeting physical activity guidelines, reporting >2 h/day of non-academic screen time on weekends, and living in neighbourhoods with low disadvantage. Risk factors related to individual lifestyle behaviours are generally consistent, and neighbourhood factors generally inconsistent, as children age from late childhood to pre-adolescence. Multiple factors from developmental, behavioural and contextual domains increase risk for excess sedentary behaviour; these warrant consideration to devise effective prevention or management strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".