Identification and characterization of screen use trajectories from late childhood to adolescence in a US-population based cohort study
Bibliographic record
Abstract
Screen use is a known risk factor for adverse physical and mental health outcomes during childhood and adolescence. Moreover, racial/ethnic disparity in screen use persists among adolescents. However, limited studies have characterized the population sharing similar longitudinal patterns of screen use from childhood to adolescence. This study will identify and characterize the subgroups of adolescents sharing similar trajectories of screen use from childhood to adolescence. Study participants of the Adolescent Brain Cognitive Development Study (2016-2021) in the U.S with non-missing responses on self-reported screen use at each year of the study were included in the analysis. Growth mixture modeling was used to identify the optimal number of subgroups of adolescents with similar trajectories. Subsequently, socio-demographic characteristics, familial background, and perceived racism and discrimination during childhood was assessed for each subgroup population. Perceived discrimination was measured using the Perceived Discrimination Scale. There were two major subgroups of individuals sharing similar trajectories of screen use: Drastically Increasing group (N = 1333); Gradually Increasing group (N = 10336). Higher proportions of the Drastically Increasing group were racial/ethnic minorities (70%) as compared to the Gradually Increasing group (45%). Moreover, the Drastically Increasing group had higher proportions of individuals reporting perceived racism and discrimination during childhood.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".