Digital media use, social isolation, and well-being in adolescents: A network analysis
Bibliographic record
Abstract
Digital media are ever-present in the social lives of adolescents. Some previous work suggests that screen time (i.e., the total amount of time spent on screen-based devices) may generally be detrimental to youth's mental well-being and interpersonal relationships by way of displacing in-person interactions and increasing loneliness (Twenge et al., 2019). In contrast, other theories suggest that some forms of media use may be conducive to well-being and less social isolation (Huang et al., 2022). These findings suggest that the extent to which digital media use relates to positive or negative outcomes varies based on the purpose or modality of device use. However, nuanced associations have not been well-explored. More comprehensive analytical approaches that enable simultaneous assessments of both positive and negative outcomes are required to fully capture the complex interrelationships between media use, mental health, and social well-being. This study examines the associations between various aspects of digital media use, social isolation, and psychological outcomes in a sample of Canadian adolescents. Findings will bolster the current understanding of how digital media permeates young peoples' social and emotional well-being.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".