INTERNET ADDICTION AND ITS IMPACT ON PHYSICAL HEALTH
Bibliographic record
Abstract
Aims: Internet addiction, a recently emerged term in medical literature, has significant physical effects on the young generation. In this research, controversial effects of internet addiction on physical health have been investigated among the students of Trakya University School of Medicine, who constitute a part of the population at risk. Methods: The study included 327 medical students. The correlation between internet addiction and physical complaints associated with internet usage and its relation with gender, purpose and duration of internet usage were investigated. The data were obtained by using surveys and Internet Addiction Scale. To evaluate the data; descriptive statistics, Correlation, Mann-Whitney U tests, Cronbach alpha methods and survey with 16 questions were used for statistical analysis. Results: There is a statistically significant difference in terms of Internet Addiction Scale score between internet addiction and physical complaints such as headache, feeling of stiffness, backache, neck pain and insomnia. Internet Addiction Scale score and time spent on the internet showed a statistically significant correlation. Conclusion: Increase in internet usage leads to many physical health problems, which may cause serious and permanent damage to physical health. Therefore, the required attention must be given to this subject especially for the benefit of younger generations.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".