The interconnection between social media addiction, alexithymia and empathy in medical students
Bibliographic record
Abstract
Introduction: This study explores whether high alexithymia values correlate with low levels of empathy, while also trying to identify potential connections with social media addiction. Methods: We hypothesized that alexithymia mediates the relationship between social media addiction and empathy levels in a sample of undergraduate students. The study population consisted of 649 medical students in the 4th/5th/6th University year, recruited between March and May 2021. For this assessment, we employed three psychometric instruments: the Toronto Empathy Questionnaire (TEQ), the Social Media Addiction Scale-Student Form (SMAS-SF), and the Toronto Alexithymia Scale (TAS-20). A pathway analysis investigated alexithymia as a mediator between social media addiction and the degree of empathy in medical undergraduates. Sobel's test and the Baron and Kenny approach were used for testing mediation. Results: The TEQ total mean score was 48.76 ± 5.65, while the TAS-20 total mean score was 47.71 ± 11.49. Further analysis of the TAS-20 scale scores showed that 21.42% of students had possible alexithymia, while 14.02% had clear alexithymia. The SMAS-SF total mean score was 73.20 ± 14.59. None of the students reported levels consistent with major social media addiction. The mediated effect of the TAS-20 is about 1.3 times larger than the direct effect of the SMAS-SF on TEQ. Discussion: We found a significant negative correlation between empathy and alexithymia in medical students. Alexithymia was a mediator between social media addiction and empathy. Therefore, we recommend further efforts to identify potential levels of alexithymia in medical students, in order to successfully develop tailored interventions aimed at increasing their emotional awareness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".