Digital Media and Developing Brains: Concerns and Opportunities
Bibliographic record
Abstract
Purpose of Review: The incorporation of digital technologies and their use in youth's everyday lives has been increasing rapidly over the past several decades with possible impacts on youth development and mental health. This narrative review aimed to consider how the use of digital technologies may be influencing brain development underlying adaptive and maladaptive screen-related behaviors. Recent Findings: To explore and provide direction for further scientific inquiry, an international group of experts considered what is known, important gaps in knowledge, and how a research agenda might be pursued regarding relationships between screen media activity and neurodevelopment from infancy through childhood and adolescence. While an understanding of brain-behavior relationships involving screen media activity has been emerging, significant gaps exist that have important implications for the health of developing youth. Summary: Specific considerations regarding brain-behavior relationships involving screen media activity exist for infancy, toddlerhood, and early childhood; middle childhood; and adolescence. Transdiagnostic frameworks may provide a foundation for guiding future research efforts. Translating knowledge gained into better interventions and policy to promote healthy development is important in a rapidly changing digital technology environment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".