Internet Use and Internet Addiction Disorder among Medical Students: A Case from China
Bibliographic record
Abstract
Internet is used more and more widely and extensively among university students. In order to keep abreast of Internet use among medical students, provide basis for cultivating correct and reasonable Internet use habits of them, based on expert consultation and literature review, a “Questionnaire on University Students’ Internet Behavior” is designed by the author, method of randomly stratified cluster sampling is employed to conduct questionnaire survey on 380 students from three medical academies in Xi’an in China, and the investigation results are analyzed through statistics. According to the result, the reported rate of medical students’ Internet surfing is 92.3%; with respect to the frequencies of Internet behaviors, it differs among medical students; close attention is paid to sending & receiving e-mails, searching for information, chatting, browsing current affairs, et al; while little attention is paid to business negotiation, falling in love and sexual behavior; in the prevalence rate of Internet addiction disorder, boys’ is obviously higher than girls’. In all, Internet behaviors of medical students are characterized by high need-hierarchy, concentrated value orientation and various types. But Internet addiction disorder is also rather conspicuous. Therefore families, universities and the society shall attach importance to it and take measures if necessary.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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; 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".