Pathways from adolescent screen time to eating related symptoms: a multilevel longitudinal mediation analysis through self-esteem
Bibliographic record
Abstract
OBJECTIVE: Screen time and self-esteem have been shown to be important correlates of eating disorders in adolescence. However, there is an absence of longitudinal studies that distinguish between time-varying factors, accounting for parallel developmental changes and common underlying vulnerability. DESIGN: A total of 3,801 adolescents were administered self-report measures, annually, over the course of 5 years. The association of screen time (social media use, television watching, video gaming) on eating related symptoms was analyzed using a longitudinal Bayesian multilevel path analysis framework. Self-esteem was examined as a mediating factor in this model. This study investigated direct and indirect associations at between-person, concurrent within-person, and lagged-within-person levels, while controlling for gender. RESULTS: The findings revealed that all types of screen time exposure were significantly associated with eating related symptoms at between and within-person levels. A significant association at the lagged-within person level was only revealed for social media use. Self-esteem was found to be a significant mediating factor between screen time and eating related symptoms. CONCLUSION: An increase in social media use one year was associated with increased of eating related symptoms two years later through lower self-esteem. Implications for prevention 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".