Cambios en el uso de medios digitales de los niños y variables parentales durante la pandemia de COVID-19
Bibliographic record
Abstract
Child attention skills are critical for supporting self-regulation abilities, especially during the first years of life. On the other hand, inattention symptoms in preschoolers have been associated with poor school readiness, literacy skills and academic achievement. Previous research has linked excessive screen time with increased inattention symptoms in early childhood. However, most research has only focused on TV exposure and did not investigate this association during the COVID-19 pandemic. This atypical context has increased screen time in children worldwide, including preschoolers. This study aimed to investigate the prospective association between child screen time and parenting stress when children were 3.5 and later inattention symptoms at age 4.5. Participants were parents of Canadian preschoolers that reported screen media use, parenting stress, and individual child and family characteristics during the pandemic (N=315, 2020) and inattention symptoms one year later (N=264). Analyses using multiple linear regression, revealed a positive association between child screen time at age 3.5 and inattention symptoms at 4.5 years. Parental stress was also positively associated with child inattention symptoms. Associations were observed above individual (child age, inhibitory control, and sex) and family (parent education and family income) characteristics. These results highlight that preschooler screen use and parenting stress may undermine attentional skills. Practical implications are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".