The relationship between technology addictions and schizotypal traits: mediating roles of depression, anxiety, and stress
Bibliographic record
Abstract
BACKGROUND: The way how technology addiction relates to psychosis remains inconclusive and uncertain. The present study aimed to test the hypothesis of a mediating role of depression, anxiety and stress in the association between three technology (behavioral) addictions (i.e., Addiction to the Internet, smartphones and Facebook) and psychosis proneness as estimated through schizotypal traits in emerging adults. METHODS: A cross-sectional study was performed among non-clinical Tunisian university students (67.6% females, mean age of 21.5 ± 2.5 years) using a paper-and-pencil self-administered questionnaire. RESULTS: Results for the Pearson correlation revealed that higher smartphone, Internet, and Facebook addictions' scores were significantly and positively correlated with each of the depression, anxiety and stress subscores; whereas depression (r = 0.474), anxiety (r = 0.499) and stress (r = 0.461) scores were positively correlated with higher schizotypal traits. The results of the mediation analysis found a significant mediating effect for depressive, anxiety and stress symptoms on the cross-sectional relationship between each facet of the TA and schizotypal traits. CONCLUSION: Our findings preliminarily suggest that an addictive use of smartphones, Internet and Facebook may act as a stressor that exacerbates psychosis proneness directly or indirectly through distress. Although future longitudinal research is needed to determine causality, we draw attention to the possibility that treating psychological distress may constitute an effective target of interventions to prevent psychosis in adolescents with technology addictions.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".