Internet Addiction among Senior Medical Students in King Abdulaziz University, Prevalence and Association with Depression
Bibliographic record
Abstract
INTRODUCTION: Excessive internet use can lead to negative outcomes such as poor academic performance and social isolation. Objectives: To estimate the prevalence of the internet addiction and to explore the factors associated with depression among medical students, King Abdul-Aziz University, Jeddah, Saudi Arabia.METHODS: The total number of senior medical students was 1049 in the academic years 2013-2014. An analytical cross sectional study was adopted. Stratified sampling technique with proportional allocation to recruit medical students. A self-administered questionnaire was used which adopted the 20-item Young’s internet addiction test (IAT) to explore the internet addictions, while the existence of depression was assessed using the centre for epidemiological studies depression scale (CES-D).RESULTS: The study included 161 medical students, making the response rate of 78.2%. Majority (94.4%) had computer and 99.4% were using the internet. Community sites ranked first (40.6%), whereas general sites, chatting and emailing were preferred by 14.4%, 10% and 10% respectively. Internet addiction was reported among only five students (3.1%). Possible addiction was reported among 74 students (46.3%). Male students (66.2%) were more addicts to internet than females (44.6%) (P=0.007). The 4th year students reported the highest rate of internet addiction or possible addiction (70.3%) (P=0.003). All internet addicts were depressed, whereas 74.1% of possible addicts and 62.2% of non addicts were depressed (P=0.088). However, the trend in the prevalence of depression in the three different situations was statistically significant (P=0.034).CONCLUSION: Internet addiction is growing hidden problem, which has psychological and social impact on medical students and requires preventive strategies and therapeutic interventions.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".