The Association between Problematic Internet Use, Suicide Probability, Alexithymia and Loneliness among Turkish Medical Students
Bibliographic record
Abstract
OBJECTIVE: It is known that problematic internet use (PIU) increasing especially among the youth and has become an important public health problem. The aim of the present study was to investigate the prevalence of PIU among the medical students and the relationship between PIU and selected socio-demographic characteristics (e.g. gender), loneliness, alexithymia and probability of suicide. METHOD: A total of 328 subjects (44.2% males, 55.8% females) completed four instruments: Young Internet Addiction Test (YIAT), UCLA loneliness scale (UCLA-LS), The 20-item Toronto Alexithymia Scale (TAS-20) and Suicide Probability Scale (SPS). RESULTS: PIU was detected in 6.4% (n=21) of the participants. Its prevalence was significantly higher in males than in females (p=0.009). We found significant positive correlation between loneliness, alexithymia, suicide probability and PIU. A significant positive relationship was also found between PIU and Hopelessness, Suicide Ideation and Hostility. CONCLUSION: PIU was found at a higher rate in male gender and was found to be associated with loneliness, alexithymia and probability of suicide. Prospective studies need to be based on different sampling groups to understand the underlying mechanisms that affect PIU and to explore effective preventative treatment strategies. Keywords: Alexithymia; Internet; Medical students; Loneliness; Suicide Language: en
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".