Mobile Phone Addiction and Career Preparation in College Students
Bibliographic record
Abstract
To explore the status and relationship of college students' mobile phone addiction（MPA） and career preparation, 337 Chinese college students were surveyed on mobile phone usage， mobile phone addiction and career preparation through the Internet. The results showed that all college students used mobile phones, 9.76% of them used two mobile phones and the rest used one mobile phone; 10% of college students spent 100 RMB or more per month on mobile phones, 63.31% of them spent 30-100 RMB, 26.63% of them spent 30 RMB or less; 51.48% of college students used mobile phones for more than 5 hours per day, 83.72% of them used it for more than 3 hours per day; 90% of college students used mobile phones before bed and during rest, 67.75% of them used it as soon as they woke up in the morning, 65.38% of them used it when toilets, 64.5% of them used it when eating, 52.66% of them used it when walking. The top five uses of mobile phones for college students from high to low were Wechat, Payment, Shopping, Information Access and Weibo. 36.77%% of college students have serious mobile phone addiction and that girls were significantly higher than boys in MPA and out of control of its sub-dimension. The career preparation and its dimensions of college students were at a medium level. There is a significant negative correlation between college students' MPA and employment preparation. So mobile phone addiction has a certain degree of adverse impact on the career preparation of college students.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".