Screen time among kindergarten children in the Australian Capital Territory (including a comparison of First Nations and non-Indigenous children)
Bibliographic record
Abstract
BACKGROUND: Australian First Nations children have poorer outcomes across a range of health measures. High levels of screen time are associated with a range of adverse health outcomes. Gaps remain in our knowledge on the association between screen time and health outcomes for different demographic groups, including First Nations children. We aimed to describe the screen time behaviours of First Nations and non-Indigenous kindergarten children in the Australian Capital Territory (ACT) and identify any associated disparities in key health outcomes. METHODS: 5,516 children participated in the cross-sectional population-based ACT Kindergarten Health Check (KHC) survey, including 146 First Nations children. The association between screen time of more than two hours per day and health outcomes (body mass index, respiratory health, development and wellbeing) was assessed for both First Nations and non-Indigenous cohorts using comparative statistics. RESULTS: Non-Indigenous children who had an average of more than two hours of screen time per day were 1.5 (95% CI, 1.3-1.7) times more likely to be overweight/obese, 1.2 (95% CI, 1.0-1.4) times more likely to have had a wheeze/whistle in their chest in the 12 months before the KHC, 1.5 (95% CI, 1.3-1.7) times more likely to have developmental concerns and 1.5 (95% CI, 1.3-1.8) times more likely to have reduced wellbeing measures compared to non-Indigenous children with screen time of two hours or less per day. None of these associations were statistically significant amongst First Nations children. CONCLUSIONS: Screen time of more than two hours per day is associated adverse health outcomes in non-Indigenous children, including overweight/obesity, respiratory problems, developmental concerns, and reduced wellbeing. Further analysis is required to enable a more robust assessment of the effect of screen time on health outcomes for First Nations children.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".