Cumulative Social Risk and Child Screen Use: The Role of Child Temperament
Bibliographic record
Abstract
OBJECTIVES: It is critical to understand what children, and in which context, are at risk for high levels of screen use. This study examines whether child temperament interacts with cumulative social risk to predict young children's screen use and if the results are consistent with differential susceptibility or diathesis-stress models. METHODS: Data from 1,992 families in Calgary, Alberta (81% White; 47% female; 94% >$40,000 income) from the All Our Families cohort were included. Mothers reported on cumulative social risk (e.g., low income and education, maternal depression) at <25 weeks of gestation, child's temperament at 36 months of age (surgency/extraversion, negative affectivity, effortful control), and child's screen use (hours/day) at 60 months of age. Along with socio-demographic factors, baseline levels of screen use were included as covariates. RESULTS: Children high in surgency (i.e., high-intensity pleasure, impulsivity) had greater screen use than children low in surgency as social risk exposure increased. In line with differential susceptibility, children high in surgency also had less screen use than children low in surgency in contexts of low social risk. Children with heightened negative affectivity (i.e., frequent expressions of fear/frustration) had greater screen use as social risk increased, supporting a diathesis-stress model. CONCLUSIONS: Young children predisposed to high-intensity pleasure seeking and negative affectivity in environments characterized as high in social risk may be prone to greater durations of screen use. Findings suggest that an understanding of social risks and individual characteristics of the child should be considered when promoting healthy digital health habits.
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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 | 0.000 | 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".