Income inequality and adherence to 24-hour movement guideline recommendations among adolescents: a multilevel growth curve analysis using longitudinal data from three waves of the Cannabis, Obesity, Mental health, Physical activity, Sedentary behaviour and Smoking (COMPASS) study (2016–2019)
Bibliographic record
Abstract
BACKGROUND: There is a paucity of literature regarding income inequality and adolescent movement behaviours (physical activity, sedentary behaviours, sleep). This study examined whether income inequality was associated with meeting Canadian 24-Hour Movement Guidelines (24HMG) recommendations among adolescents over time. METHODS: Longitudinal data from adolescents (n=9299) in the Cannabis, Obesity, Mental health, Physical activity, Sedentary behaviour and Smoking study (2016-2017 to 2018-2019) were linked with income data at the census division (CD) level from the 2016 Canadian Census. Adolescents (aged 13-19 years) reported on their physical activity, sleep duration and screen time via questionnaire. Gini coefficients were calculated at the CD level using after-tax household income from the 2016 Canadian Census. Multilevel logistic regression analyses were performed to test the association between income inequality and meeting several 24HMG recommendations. RESULTS: The joint effect (income inequality*time) was significant for meeting the sleep duration recommendation (2017-2018 OR=0.83, 95% CI 0.74, 0.92; 2018-2019 OR=0.77, 95% CI 0.70, 0.86; p<0.0001), meeting any two recommendations over time (2017-2018 OR=0.97, 95% CI 0.86, 1.09; 2018-2019 OR=0.85, 95% CI 0.75, 0.97; p=0.0402) and meeting combined sleep and physical activity recommendations (2017-2018 OR=0.93, 95% CI 0.82, 1.06; 2018-2019 OR=0.82, 95% CI 0.71, 0.94; p=0.0200). Joint effects (income inequality*time) were not significant (p>0.05) for screen time or physical activity independently of sleep. CONCLUSION: Adolescents attending schools in areas with greater income inequality may be at higher risk for inadequate sleep and combined short sleep and physical inactivity.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".