Social Media Addiction in Medical Faculty Students; the relationship with dissociation, social anxiety, and alexithymia
Bibliographic record
Abstract
Objective: The aim of this study was to evaluate social media addiction in medical faculty students and the relationships with dissociation and social anxiety experienced in social media use and the level of alexithymia. Methods: 329 students who agreed to participate in the research completed the following scales; Bergen Social Media Addiction Scale (BSMAS), Toronto Alexithymia Scale (TAS-20), Van Online Dissociative Experiences Scale (VODES), Social Anxiety Scale for Social Media Users (SAS-SMU). Results: The 4 sub-scales of the SAS-SMU, the 3 sub-scales of the TAS, and the VODES were analyzed as independent variables and the BSMAS was evaluated as a dependent variable. According to this, social media addiction was affected by the shared content anxiety and self-assessment anxiety sub-scale points of the SAS-SMU, and by the VODES points. Shared content anxiety was determined to predict social media addiction positively and significantly (β=0.264, t (320) = 3.16, p=0.002). Self-assessment anxiety was determined to predict social media addiction positively and significantly (β=0.169, t (320) = 2.23, p=0.026). Online dissociative experiences was determined to predict social media addiction positively and significantly (β=0.217, t (320) = 4.15, p
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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 | 0.001 |
| 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) | 0.003 | 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".