Reducing harm and promoting positive media use strategies: new perspectives in understanding the impact of preschooler media use on health and development
Bibliographic record
Abstract
Most children grow up in homes with easy access to multiple screens. Screen use by children between the ages of 0 to 5 has become a worldwide preoccupation. In the present narrative review, we examine child and parent screen use and its contribution to physical, cognitive, and social developmental outcomes. As research has mostly focused on the adverse consequences of screen media, we aim to depict both the negative and the positive influences of screen usage. To provide a more nuanced portrait of the potential benefits and harms of screen use, we examine how consequences of media use vary according to the content of media (ex., educational, violent), context (ex., using screens during mealtimes), and the nature (ex., passive vs active use) of child screen use. Our review supports existing screen time guidelines and recommendations and suggests that media content, the context of use, and the nature of child use, as well as the parent's own screen use, be considered clinically. Future research should seek to clarify how these dimensions jointly contribute to child screen use profiles and associated consequences. Finally, child sex, behavioral/temperamental difficulties, and family adversity appear to contribute to child screen use and its consequences and should be considered in future research. Suggestions for harm-reduction approaches 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".