A Natural Experiment of Childhood Lifestyle on Emotional Distress Outcomes Using a Birth Cohort of Typically Developing Children
Bibliographic record
Abstract
PURPOSE: Many youngsters have an unhealthy lifestyle. This raises risks of emotional distress (i.e., depressive and anxiety symptoms). Conversely, those with emotional distress are also at risk of having an unhealthy lifestyle. We aim to examine the association between emotional distress and lifestyle in children. METHODS: Data were from the Quebec Longitudinal Study of Child Development, which was collected in Quebec province, Canada. Lifestyle and emotional distress were assessed by questionnaires completed by mothers, teachers, and children at ages 8 ( N = 1451, 52% girls), 10 ( N = 1334), and 12 years ( N = 1355). Latent profile analysis was used to derive lifestyle profiles. Logistic regression and mean difference analysis were conducted to test the associations between emotional distress and lifestyle profiles. RESULTS: At age 10 years, four profiles were found: healthy (43 %); fair (38 %); inactive (15 %); and sedentary (4 %). Children in the sedentary lifestyle profile had significantly more depressive symptoms at age 12 years compared with the healthy lifestyle profiles (mean difference [MD] = -1.618, 95% confidence interval [CI] = -2.841 to -0.396, P = 0.009), the fair lifestyle profile (MD = -1.399, 95% CI = -2.629 to -0.170, P = 0.03), and the inactive lifestyle profile (MD = -1.609, 95% CI = -2.966 to -0.253, P = 0.02). Effect sizes were large (respectively d = 1.13, d = 0.97, and d = 1.12). CONCLUSIONS: Parents and schools should provide more opportunities for extracurricular physical activity from an early age. The study would have benefited from including distinct types of sport and electronic devices instead of aggregated variables to evaluate differential associations with emotional distress.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| 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".