Internet Addiction among Undergraduate Medical Students and Its Relationship with Alexithymia, Stress, Anxiety, and Depression in an Indian Medical College: A Cross-sectional Study
Bibliographic record
Abstract
Abstract Context: Internet addiction (IA) is a rising issue, particularly among university students. The presence of comorbid psychiatric distress can worsen the impact of IA on academic and social functioning. Alexithymia, difficulty recognizing and expressing emotions may play a role in this relationship. Aims: This study aimed to assess the severity and association of IA, psychological distress, and alexithymia among undergraduate medical students in India. Settings and Design: This was a cross-sectional study of 380 undergraduate medical students following convenience sampling, studying in Mysore Medical College and Research Institute (MMCRI), Mysuru. Subjects and Methods: Sociodemographic details were collected, and participants completed the Young’s Internet Addiction Test (IAT-20), Depression, Anxiety, and Stress Scale-21 (DASS-21), and Toronto Alexithymia Scale-20 (TAS-20). Statistical Analysis: Statistical analysis was performed using SPSS-20. Descriptive statistics expressed as frequencies, means, and percentages. Qualitative data were analyzed using the Chi-square test to find out the association between two categorical variables. Spearman’s rank correlation test was used to find the correlation involving ordinal variables. Statistical significance was set at p <.05. Results: About 45.7% were aged 18–20 years, male (58.4%), and from urban areas (54.2%). On IAT, 26.6% showed mild addiction, 11.6% had moderate addiction, and 1.3% had severe dependence. Psychological distress with 42.1% experienced depression, 42.4% anxiety, and 20.0% stress symptoms. In addition, 25.0% were classified as alexithymia. Significant correlations were found between grades of depression, anxiety, stress, and alexithymia with gender, residence, psychiatric illness in family, substance, and Internet use. Conclusions: The study population revealed a high prevalence of IA and psychiatric distress among undergraduate medical students. These findings highlight the need for interventions and support services targeting IA and related psychological distress among medical students.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".