The co-occurrence between depressive symptoms and smartphone addiction: a network analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study aimed to explore the co-occurrence between depression and smartphone addiction (SA) from the network perspective as well as the network invariance across different groups. A total of 8347 Chinese college students were included in the study. Network analysis was conducted to estimate the network structure of the co-occurrence between depression and SA symptoms. A network comparison test was utilized to explore sex, severity of depression and SA variations in the network structures and strengths. D18 ‘Sad’ was the central symptom for the estimated network. D5 ‘Mind’ may be the most important bridge symptom between depression and SA. Males and females differed in the distribution of edge weights (M = 0.103, p = 0.024). Students with or without depressive symptoms showed significant differences in the distribution of edge weights (M = 0.129, p = 0.001) and global strength (14.5 vs. 13.5, p = 0.006). In addition, there are variations in the distribution of edge weights for college students in different SA severity groups (M = 0.119, p < 0.001). Attention to these core and bridge symptoms may decrease the odds of co-occurrence of depressive symptoms and SA. Interventions to address the co-occurrence should also take into account sex and severity of depression and SA.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it