Internet use and addiction among medical students of Universiti Sultan Zainal Abidin, Malaysia
Bibliographic record
Abstract
BACKGROUND: The use of Internet has now become indispensable, and the technology has revolutionized the medical education and practice worldwide. Currently, medical students and professionals have an enormous opportunity to keep them always updated with the exponential growth of knowledge because of potential progression of Internet throughout the world that enables them to become a lifelong learner. Internet addiction is a widespread phenomenon among students and academicians at universities in Malaysia. Students use the Internet for recreational purpose and personal and professional development. The Internet has become an integral part of day-to-day life of the university students, including medical students. The aim of the present study was to examine the Internet use and addiction among students of Universiti Sultan Zainal Abidin, Malaysia. METHODS: This was a cross-sectional study in which a questionnaire, Internet Addiction Diagnostic Questionnaire, developed by the Center for Internet Addiction, USA, was used. One hundred forty-nine medical students of Universiti Sultan Zainal Abidin participated in this study. Data were analyzed using Statistical Package for the Social Sciences software. RESULTS: The mean scores were 44.9±14.05 and 41.4±13.05 for male and female participants, respectively, which indicated that both the genders were suffering from mild Internet addiction. CONCLUSION: =0.007). Overall, from the research data and having worked with this cohort very closely, Universiti Sultan Zainal Abidin medical students can be labeled as wonted and recurring users of the Internet. Nevertheless, it is very difficult to define as Internet addicts or pathological users of the Internet because of small sample size and cross-sectional study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".