Early childhood screen use and symptoms of problematic media use
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective: To assess associations between early childhood screen time trajectories and problematic media use scores by age 5.5. Methods: The present study is based on a prospective, community-based convenience sample of 315 parents of preschoolers, from Canada studied at the ages of 3.5 (2020), 4.5 (2021), and 5.5 (2022) during the Covid-19 pandemic. Parent-reported screen use at the ages of 3.5, 4.5, and 5.5 was used to estimate preschooler screen use trajectories. Using latent growth modeling, we identified low (mean = 0.9 h/day, 23%), average (mean = 3.0 h/day, 56%), and high (mean = 6.38 h/day, 21%) screen time trajectories. Parents reported child problematic media using the Problematic Media Use Measure - Short Form (PMUM-SF). Results: A multiple regression, adjusted for child sex, effortful control and parent education and stress revealed that compared to children in the low screen time trajectory, children in the high screen time trajectory had higher problematic media use scores at age 5.5 (β = 0.378, p < 0.001). In addition, children in the average screen time trajectory scored higher than children in the low screen time trajectory (β = 0.229, p ≤ 0.001). Conclusion: Our findings suggest that higher screen use in early childhood is associated with an increased risk for the development of dysregulated media use, which can interfere with family functioning. As such, parents should be encouraged to follow screen time recommendations of ≤1 h/day for children between the ages of 2 and 5.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it