The Mediating Role of Visual Stimuli From Media Use at Bedtime on Psychological Distress and Fatigue in College Students: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Empirical research has linked psychological distress with fatigue. However, few studies have analyzed the factors (eg, stimuli from bedtime media use) that affect the relationship between psychological distress and fatigue. OBJECTIVE: The aim of this study was to examine whether visual stimuli from bedtime media use mediate the relationship between psychological distress and fatigue among college students. METHODS: The sample included 394 participants (92 males, 302 females) with a mean age of 19.98 years (SD 1.43 years), all of whom were Chinese college students at an occupational university in Sichuan Province, China. Data were collected using a paper-based questionnaire that addressed psychological distress, stimuli from bedtime media use, and fatigue. Mediation analysis was conducted using the PROCESS macro version 2.16.2 for SPSS 22, which provided the 95% CIs. RESULTS: Both psychological distress (r=.43, P<.001) and visual stimuli from bedtime media use (r=.16, P<.001) were positively related to fatigue. The association between auditory stimuli from bedtime media use and fatigue was not significant (r=.09, P=.08). The relationship between psychological distress and fatigue was partially mediated by visual stimuli from bedtime media use (beta=.01, SE 0.01, 95% CI 0.0023-0.0253). CONCLUSIONS: The findings imply that psychological distress has an indirect effect on fatigue via visual stimuli from bedtime media use. In contrast, auditory stimuli from bedtime media use did not have the same effect. We suggest that college students should reduce bedtime media use, and this could be achieved as part of an overall strategy to improve health. Mobile health apps could be an option to improving young students' health in daily life.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".