Why are Individuals with Alexithymia Symptoms More Likely to Have Mobile Phone Addiction? The Multiple Mediating Roles of Social Interaction Anxiousness and Boredom Proneness
Bibliographic record
Abstract
PURPOSE: Previous studies have investigated the relationship between alexithymia and problematic mobile phone use (PMPU). However, yet gaps in identifying the internal mechanisms of this relationship remain. Hence, based on the Interaction of Person-Affect-Cognition-Execution model, the current research examined the mediating roles of college students' social interaction anxiousness (SIA) and boredom proneness (BPS) in the relationship between alexithymia and PMPU. METHODS: = 0.97) were recruited from two universities in the southeast of China to complete a series of questionnaires, including the Toronto alexithymia scale, SIA scale, BPS scale, and mobile phone addiction index questionnaire. In order to conduct conservative predictions, the demographic variables (ie, gender) were controlled as covariates. RESULTS: The results of multiple mediation analysis showed that (1) alexithymia was positively linked with PMPU; (2) both SIA and BPS mediated the link between alexithymia and PMPU; and (3) a serial indirect pathway emerged (ie, alexithymia → SIA → BPS → PMPU). CONCLUSION: These findings indicated that alexithymia could influence PMPU in a simple indirect way (parallel mediation) and in a complex indirect way (serial mediation). Besides, these findings provide some insights into the prevention and intervention of PMPU.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".