Studying the Relationship between Quality of Sleep and Addiction to Internet among Students
Bibliographic record
Abstract
Background: In recent years, Internet use has been of interest to different groups of people especially students and its charm has caused users to spend hours of their time at the computer. Studies show that growing demand for Internet technology caused significant mental health problems and reduced quality of life and unhealthy social relationships for many people. The aim of this study was to determine the relationship between internet addictions and sleep quality as one of the components of quality of life among college students.Methods: This cross-sectional study was done among students of Torbat Heydariyeh city in 2015. Data were collected by self-report. Data collection tools were internet addiction questionnaire, Pittsburgh Sleep Quality Index and a demographic questionnaire. Data was analyzed by SPSS 21. P <0.05 considered statically significant.Results: The average score of sleep disorders and addiction to the Internet was 4.690 ± 0.050 and 33.98 ± 12.05, which represents the average sleep disorders and internet addiction among students. 32.50% of students were suffering from sleep disorders and 23.9% of students had high dependence on the Internet and 2% had severe dependence. Between internet addiction and sleep disorders components, sleep quality and overall score of Pittsburgh questionnaire there was a significant positive correlation (P≤0/05).Conclusion: The results showed that excessive use of the internet is associated with reduced sleep quality and increased daytime sleepiness. Due to the increasing use of the Internet in Iran, especially among students, Familiarize users with the harms of excessive use of the Internet and promote the proper Correct culture is essential In order to promote the correct pattern of Internet use helped to reduce sleep problems in students.Keywords: addiction to Internet, sleep quality, Student.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".