Longitudinal Associations Between Screen Use and Reading in Preschool-Aged Children
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The home literacy environment has been identified as a key predictor of children's language, school readiness, academic achievement, and behavioral outcomes. With the increased accessibility and consumption of digital media, it is important to understand whether screen use impacts off-line enrichment activities such as reading or whether reading activities offset screen use. Using a prospective birth cohort, we examined reading and screen use at 24, 36, and 60 months to elucidate the directional association between screen use and reading over time. METHODS: This study included data from 2440 mothers and children in Calgary, Alberta, drawn from the All Our Families cohort. Children's screen use and reading activities were assessed via maternal report at age 24, 36, and 60 months. Sociodemographic covariates were also collected. RESULTS: Using a random-intercepts cross-lagged panel model, which statistically controls for individual-level confounds, this study revealed that greater screen use at 24 months was associated with lower reading at 36 months (β = -.08; 95% confidence interval: -0.13 to -0.02). In turn, lower reading at 36 months was associated with greater screen use at 60 months (β = -.11; 95% confidence interval: -0.19 to -0.02). Covariates did not modify the associations. CONCLUSIONS: A reciprocal relationship between screen use and reading was identified. Early screen use was associated with lower reading activities, resulting in greater screen use at later ages. Findings emphasize the need for practitioners and educators to discuss screen use guidelines and encourage families to engage in device-free activities to foster early literacy exposure.
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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 | 0.002 |
| 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 | 0.000 |
| 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".