The moderating role of social support in the relationship between alexithymia and problematic smartphone use among Chinese depressed adolescents: a cross-sectional study
Bibliographic record
Abstract
Background: Alexithymia is closely related to problematic smartphone use (PSU) in adolescents, but its mechanism in adolescents with depression is still unclear. The aim of this study was to investigate the predictive effect of alexithymia on PSU in depressed adolescents and to examine the moderating effect of social support (family, friends, significant others) on this relationship. Methods: A total of 2343 adolescents with depressive disorder aged 12-18 years from 14 medical institutions in China were included in this cross-sectional study. The Toronto Alexithymia Scale, Mobile Phone Addiction Index and Multidimensional Perceived Social Support Scale were used to evaluate the core variables. Hierarchical regression analysis was used to test the moderating effect after controlling demographic variables. Results: Alexithymia was significantly and positively associated with PSU (r = 0.343, p < 0.01), with Difficulty in Recognizing Feelings having the strongest association (r = 0.348). Stratified regression revealed that family support (B = -0.625, p = 0.005) and friend support (B = -0.577, p = 0.013) significantly attenuated the positive predictive effect of alexithymia on PSU, whereas there was no significant moderating effect of significant others support. Conclusion: Research shows that social support mitigates the risk of (PSU) among depressed adolescents with alexithymia, with family and friend support as key protective factors. These findings highlight the need for clinical screening and interventions targeting adolescents with alexithymia and low social support, who are at high risk for PSU. Integrating family systems therapy and friend support programs into clinical interventions may enhance emotional regulation and reduce smartphone dependence, thereby improving depressive symptoms.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".