Identifying Risk Profiles for Nonadherence to the 24-Hour Movement Guidelines for Children and Youth 6 Months Into the COVID-19 Pandemic
Bibliographic record
Abstract
PURPOSE: The purposes of the current study were to identify risk profiles for nonadherence among children and youth (5-17 y) at the 6-month mark of the COVID-19 pandemic and to discuss similarities and differences between risk profiles identified in the current study and those identified at the 1-month mark of the pandemic. METHODS: Data were part of a nationally representative sample of 1143 parents (Mage = 43.07 y, SD = 8.16) of children and youth (5-17 y) living in Canada. Survey data were collected in October 2020. RESULTS: Results showed that 3.8% met all movement behavior recommendations, 16.2% met the physical activity recommendation, 27% met the screen time recommendation, and 63.8% met the sleep recommendation. Characteristics associated with nonadherence to all movement behaviors included low parental perceived capability to restrict screen time and decreased overall time spent outdoors. Characteristics associated with nonadherence to the physical activity and screen time recommendations included youth (12-17 y), low parental perceived capability to restrict screen time, decreased time spent outdoors, and increased screen time. CONCLUSION: Results emphasized the importance of parental perceived capability to restrict screen time and children's and youth's outdoor time and showed that pandemic-related factors have impacted children and youth differently.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".