The Association between Alexithymia and Social Media Addiction: Exploring the Role of Dysmorphic Symptoms, Symptoms Interference, and Self-Esteem, Controlling for Age and Gender
Bibliographic record
Abstract
Given the popularity of social media and the growing presence of these tools in the daily lives of individuals, research about the elements that can be linked to their problematic use appears to be of great importance. The objective of this study was to investigate the factors that may contribute to the levels of social media addiction, by focusing on the role of alexithymia, body image concern, and self-esteem, controlled for age and gender. A sample of 437 social media users (32.5% men, 67.5% women; Mage = 33.44 years, SD = 13.284) completed an online survey, including the Bergen Social Media Addiction Scale, Body Image Concern Inventory, Rosenberg Self-Esteem Scale, and Twenty-Item Toronto Alexithymia Scale, together with a demographic questionnaire. Results showed a significant association between alexithymia and social media addiction, with the total mediation of body image concern (and more in detail, body dissatisfaction) and the significant moderation of self-esteem. Gender and age showed significant effects in these relationships. Such findings may offer further insights into the field of clinical research on social media addiction and may provide useful information for effective clinical practice.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".