Canadian children's and youth's adherence to the 24-h movement guidelines during the COVID-19 pandemic: A decision tree analysis
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to use decision tree modeling to generate profiles of children and youth who were more or less likely to meet the Canadian 24-h movement guidelines during the coronavirus disease-19 (COVID-19) outbreak. METHODS: = 45.12, SD = 7.55) of children (5-11 years old) or youth (12-17 years old). Data were collected in April 2020 via an online survey. Survey items assessed demographic, behavioral, social, micro-environmental, and macro-environmental characteristics. Four decision trees of adherence and non-adherence to all movement recommendations combined and each individual movement recommendation (physical activity [PA], screen time, and sleep) were generated. RESULTS: Results revealed specific combinations of adherence and non-adherence characteristics. Characteristics associated with adherence to the recommendation(s) included high parental perceived capability to restrict screen time, annual household income of ≥ $100,000, increases in children's and youth's outdoor PA/sport since the COVID-19 outbreak began, being a boy, having parents younger than 43 years old, and small increases in children's and youth's sleep duration since the COVID-19 outbreak began. Characteristics associated with non-adherence to the recommendation(s) included low parental perceived capability to restrict screen time, youth aged 12-17 years, decreases in children's and youth's outdoor PA/sport since the COVID-19 outbreak began, primary residences located in all provinces except Quebec, low parental perceived capability to support children's and youth's sleep and PA, and annual household income of ≤ $99,999. CONCLUSION: Our results show that specific characteristics interact to contribute to (non)adherence to the movement behavior recommendations. Results highlight the importance of targeting parents' perceived capability for the promotion of children's and youth's movement behaviors during challenging times of the COVID-19 pandemic, paying particular attention to enhancing parental perceived capability to restrict screen time.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".