Geographical Disparities of Uncertainty Stress and Life Stress Among University Students: A Study Across All Provinces in Mainland China
Bibliographic record
Abstract
The study objective was to investigate geographical variation of uncertainty stress and life stress among university students in China. Respondents comprised 11,954 students from 50 universities and 31 provinces in China's mainland. Respondents completed the extended version of Global Health Professional Student Survey (GHPSS) on Tobacco Control in China, which added additional health, mental stress, and behavioural items on original version, and regional variables were retrieved from the National Bureau of Statistics database. Both unadjusted and adjusted methods were used in the logistic regression analysis. The prevalence of high uncertainty stress was 19.60% (95% CI: 15.90%, 23.30%), while the prevalence of life stress was 8.60% (95% CI: 7.20%, 10.70%). The prevalence rates varied significantly across the 31 provinces. The random parameters for uncertainty stress and life stress were statistically significant at the 0.01 level, with values of 0.2593 and 0.3971, respectively. The geographical distribution revealed two high uncertainty stress zones between the east coast and the middle area, as well as in the west area from south to north. High life stress, on the other hand, was concentrated in the central area. Multilevel logistic regression showed province level per capita disposal income of households partly contributed to uncertainty stress (OR = 0.52, 95% CI: 0.36, 0.94) and life stress (OR = 0.59, 95% CI: 0.52, 0.89). These findings underscore the importance of environmental contribution to mental stress among university students. Given that college students' mental stress is high, there is a need for environmental measurements to prevent and address multiple perceived stress in students.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".