Smartphone Addiction and Associated Factors among Postgraduate Students in an Arabic Sample: A Cross-sectional Study.
Bibliographic record
Abstract
Abstract Background: Smartphone addiction and other behavioral addictions have been established to accompany social, physical, and mental health issues. In this article, we will be investigating the prevalence of smartphone addiction among postgraduate students as well as assessing its relationship to social demographics, depression, ADHD, and nicotine dependence. Objectives: · The prevalence of smartphone addiction among middle eastern postgraduate students.· Ascertain the associated factors of smartphone addiction.· Measure the incidence of MDD, ADHD, insomnia, and nicotine addiction among postgraduate students with smartphone addiction. Methods: A Cross-sectional online survey, a self-questionnaire is divided into six sections; Socio-demographics, the Smartphone Addiction Scale (SAS) Patient Health Questionnaire for Depression (PHQ9). Athens Insomnia Scale (AIS) the Fagerstrom Test for Cigarette Dependence Questionnaire (FTCd) and the Adult ADHD Self-Report Scale (ASRS-v1.1) Results: 51.0% of the participants had smartphone addiction. There’s a significant association between high smartphone use and MDD (p=0.001). 41.5% of smokers are addicted to smartphones (p=0.039). Smartphone addicts have about two times the risk of developing insomnia (OR= 2.113) (P= 0.013). Smartphone addicts had a significant risk of developing ADHD symptoms (OR =2.712) (P <0.001). Conclusion: Confirming several studies, we found a positive association between Insomnia, Depression, Adult ADHD, and Smartphone addiction. Therefore, we encourage the scientific community to study the impacts of smartphone addiction and the mental health of post-graduate students.
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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 | 0.042 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.013 |
| 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; both teacher heads agree on what is shown here.
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".