Sleep problems: predictor or outcome of media use among emerging adults at university?
Bibliographic record
Abstract
The pervasiveness of media use in our society has raised concerns about its potential impact on important lifestyle behaviours, including sleep. Although a number of studies have modelled poor sleep as a negative outcome of media use, a critical assessment of the literature indicates two important gaps: (i) studies have almost exclusively relied on concurrent data, and thus have not been able to assess the direction of effects; and (ii) studies have largely been conducted with children and adolescents. The purpose of the present 3-year longitudinal study, therefore, was to examine whether both sleep duration and sleep problems would be predictors or outcomes of two forms of media use (i.e. television and online social networking) among a sample of emerging adults. Participants were 942 (71.5% female) university students (M = 19.01 years, SD = 0.90) at Time 1. Survey measures, which were assessed for three consecutive years starting in the first year of university, included demographics, sleep duration, sleep problems, television and online social networking use. Results of a cross-lagged model indicated that the association between sleep problems and media use was statistically significant: sleep problems predicted longer time spent watching television and on social networking websites, but not vice versa. Contrary to our hypotheses, sleep duration was not associated with media use. Our findings indicate no negative effects of media use on sleep among emerging adults, but instead suggest that emerging adults appear to seek out media as a means of coping with their sleep problems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".