Impact of Perceived Unfairness, Uncertainty, and Life Stress on Urban Residents’ Health in China: Cross-Sectional Study
Bibliographic record
Abstract
Background: The prevalence of stress factors in urban settings has increased, with significant potential impact on health outcomes. Objective: This study aimed to explore the effect of perceived unfairness, uncertainty, and life stress on self-reported health status, short-term illness, and noncommunicable chronic diseases (NCDs). Methods: A cross-sectional study using multistage stratified sampling was carried out in Xi'an, Hangzhou, Guangzhou, and Guiyang in China in July 2021, and 2851 participants were included in data analysis. Self-reported health status, short-term illness, and NCDs were assessed using self-administered questionnaires. Perceived life stress, unfairness stress, and uncertainty stress were measured using a standard scale. Descriptive statistics were used to analyze participants' demographic characteristics, and chi-square tests clarified statistical differences in self-reported health status, short-term illness, and NCDs based on these characteristics. Multiple logistic regression models were constructed to measure the effects of perceived unfairness stress, uncertainty stress, and life stress on self-reported health status, short-term illness, and NCDs. Results: Severe uncertainty stress (odds ratio [OR] 1.230, 95% CI 1.007-1.503) and severe life stress (OR 1.728, 95% CI 1.411-2.118) were associated with a higher likelihood of poor self-reported health status. Severe uncertainty stress (OR 1.565, 95% CI 1.270-1.929) and severe life stress (OR 1.404, 95% CI 1.136-1.731) increased the odds of short-term illness. In addition, severe unfairness stress (OR 1.306, 95% CI 1.053-1.620), severe uncertainty stress (OR 1.542, 95% CI 1.248-1.905), and severe life stress (OR 1.344, 95% CI 1.084-1.667) were linked to a higher prevalence of NCDs. Conclusions: To conclude, severe uncertainty and life stress were both associated with increased odds of poor health outcomes (self-reported health status, short-term illness, and NCDs), while severe unfairness only affected NCDs. The findings of this study may serve as an empirical reference for the improvement of self-reported illness among Chinese urban residents.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".