Mobile phone dependence and risk factors of university students in Taian
Bibliographic record
Abstract
Objective: To understand the mobile phone dependence and risk factors of university students in Taian and to provide scientific basis for community intervention research.Methods: By stratified sampling method,550 college students from 3 universities and various majors(including science and engineering,liberal arts and medical science) were selected as research objects.The data analysis was made by using chi-square test and Logistic regression analysis.Results: The percentage of mobile phone coverage was 99.4%.The incidence of mobile phone addiction in university students was 24.7%,33.7% in science and engineering students,27.8% in liberal arts students and 13.8% in medical science students.There is a statistical discrepancy(P 0.05) caused by the difference in the students' majors.According to Logistic regression analysis,the earlier the students used cellphones,the more expensive their cellphones were and the higher possibility that they suffered from mobile phone addiction.A statistical connection between major and mobile phone addiction existed.Conclusion: A quarter of university students in Taian suffer from mobile phone addiction,and therefore it's necessary to carry out psychological counseling and behavior modification among students addicted to cellphones.Parents should avoid permitting their children to use cellphones in an early age.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".