Internet addiction, substance use and alexithymic dimensions in two different faculties' students
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to evaluate the relationship between internet addiction, substance use and alexithymia among students of education faculty and medical faculty. METHODS: This cross-sectional analytical study included 1,257 faculty students aged 18 and over, studying at Meram Medical Faculty and Ahmet Keleşoğlu Faculty of Education. Young's Internet Addiction Scale, Toronto Alexithymia Scale, Fagerström Tobacco Addiction Test and CAGE alcohol use tests were applied to collect data. RESULTS: The mean age of the participants was 21.12 ± 1.96 years, 71% (n = 893) of them were females and 29% (n = 364) were males, 37.9% (n = 477) were training at medical faculty, 62.1% (n = 780) were training at the faculty of education. Of the students, 1.5% were internet addicts, 15.3% were possible addicts, and 22.8% had alexithymia. Internet addiction was higher in those with higher alexithymia scores (p < 0.001). Internet addiction was significantly higher in male students, the third grade, ones with lower academic success, students who work their lessons less than 2 hours a week. Internet addiction was also significantly higher in smokers and alcohol users (p < 0.001). While there was a low negative correlation between the first internet using age and internet addiction (p < 0.001), there was a moderately significant positive correlation between spending uninterrupted time on the internet and internet addiction (p < 0.001). CONCLUSION: In this study, it was determined that the teacher and doctor candidates, who are studying at the faculties of education and medicine, were at risk of internet addiction. A teacher or a doctor who cannot develop social skills due to excessive internet use will not be a good model to communicate correctly with the target population.
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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 | 0.001 |
| 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".