A Cross Cultural Study of Mental Health among Internet Addicted and Non-Internet Addicted: Iranian and Indian Students
Bibliographic record
Abstract
INTRODUCTION: In the background of increasing use of internet in Asian countries, the study of psychological health in internet addicted users seems to be vital and necessary. Therefore the present study aimed to determine mental health among internet addicted and non-internet addicted Iranian and Indian students. METHODS: This cross-sectional study was conducted on 400 students in various colleges from Pune and Mumbai cities of Maharashtra. Internet Addiction Test and Symptom Check List (SCL) 90-R were used. Data were analyzed using SPSS 16. RESULTS: Internet addicted students were higher on Somatization, Obsessive-compulsive, Interpersonal sensitivity, Depression, Anxiety, Hostility, Phobic anxiety, Paranoid ideation, Psychoticism than Non-internet addicted students (P<0.05). Indian students had higher score on mental health domains compared to Iranian students (P<0.05). Female students had higher scores on Somatization, Obsessive-compulsive, Anxiety, Hostility, Phobic anxiety and Psychoticism than male students (P<0.05). CONCLUSION: Psychiatrists and psychologists who are active in the field of mental hygiene must be aware of mental problems associated with Internet addiction such as depression, anxiety, obsession, hypochondria, paranoia, interpersonal sensitivity, and job and educational dissatisfaction among Internet addicts.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".