The Ubiquity of the Screen: An Overview of the Risks and Benefits of Screen Time in Our Modern World
Bibliographic record
Abstract
ABSTRACT Sedentary behavior, and specifically screen-based sedentary behavior, has been a focus for health researchers, engineers, telecommunications companies, gamers, and the media for many years. In recent years, research in this area has proliferated at an exponential rate. On one side, arguments have been made that screen time is harmful to the healthy growth and development of children and youth. On the other side, modern technology has far surpassed any prediction of success and become a fixture of daily living, making life easier and providing opportunities never thought possible. Regardless, screens have become omnipresent in our society, and it is important to understand the risks and the benefits associated with their use. Excessive time spent in various sedentary behaviors can coexists in a lifestyle that includes sufficient levels of moderate- to vigorous-intensity physical activity, but research has shown that for optimal health benefits, individuals should be both physically active and limit their sedentary behaviors (especially screen time). This narrative review provides a brief history of research on sedentary behavior in the context of screen time, the evolution of screens and screen time, highlights the risks and benefits of screen-based sedentary behavior, and provides experimental evidence for reductions in habitual screen time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".