Early childhood screen use and symptoms of problematic media use
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".