The impact of daycare attendance on outdoor free play in young children
Bibliographic record
Abstract
Background: Outdoor free play is important for healthy growth and development in early childhood. Recent studies suggest that the majority of time spent in daycare is sedentary. The objective of this study was to determine whether there was an association between daycare attendance and parent-reported outdoor free play. Methods: Healthy children aged 1-5 years recruited to The Applied Research Group for Kids! (TARGet Kids!), a primary care research network, were included. Parents reported daycare use, outdoor free play and potential confounding variables. Multivariable linear regression was used to determine the association between daycare attendance and outdoor free play, adjusted for age, sex, maternal ethnicity, maternal education, neighborhood income and season. Results: There were 2810 children included in this study. Children aged 1 to <3 years (n = 1388) and ≥3 to 5 years (n = 1284) who attended daycare had 14.70 min less (95% CI -20.52, -8.87; P < 0.01) and 9.44 min less (95% CI -13.67, -5.20; P < 0.01) per day of outdoor free play compared with children who did not attend daycare, respectively. Conclusions: Children who spend more time in daycare have less parent-reported outdoor free play. Parents may be relying on daycare to provide opportunity for outdoor free play and interventions to promote increased active play opportunities outside of daycare are needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".