The Association Between Personality Traits and Health-Related Quality of Life and the Mediating Role of Smoking: Nationwide Cross-Sectional Study
Bibliographic record
Abstract
Background: There are positive and negative correlations in different directions between smoking, personality traits, and health-related quality of life (HRQOL), where smoking may mask the pathway between personality traits and HRQOL. Understanding the masking pathway of smoking between personality traits and HRQOL can elucidate the mechanisms of smoking's psychosocial effects and provide new ideas for developing tobacco control strategies. Objective: The purpose of this study was to investigate the correlation between Big Five personality traits and HRQOL and whether smoking mediates the relationship between them. Methods: This was a cross-sectional study using data from 21,916 respondents from the 2022 Psychology and Behavior Investigation of Chinese Residents survey. Linear regression models were used to analyze the correlations between smoking, Big Five personality traits, and HRQOL while controlling for potential confounders. The mediating role of smoking on the association between Big Five Personality traits and HRQOL was analyzed using the Sobel-Goodman mediation test. Results: Extraversion (β=.001; P=.04), agreeableness (β=.003; P<.001), and neuroticism (β=.003; P<.001) were positively correlated with HRQOL, whereas openness was negatively correlated with HRQOL (β=-.001; P=.003). Smoking was associated with a decrease in HRQOL and mediated the positive effect of HRQOL on extraversion (z=-2.482; P=.004), agreeableness (z=-2.264; P=.02), and neuroticism (z=-3.230; P=.001). Subgroup analyses further showed that smoking mediated the effect of neuroticism on HRQOL in the population with chronic illnesses (z=-2.724; P=.006), and in the population without chronic illnesses, smoking contributed to the effect of HRQOL on extraversion (z=-2.299; P=.02), agreeableness (z=-2.382; P=.02), and neuroticism (z=-2.213; P=.03). Conclusions: This study provided evidence that there is a correlation between personality traits and HRQOL. It also found that smoking plays a role in mediating the connection between personality traits and HRQOL. The development of future tobacco control strategies should consider the unique traits of each individual's personality, highlighting the significance of extraversion, agreeableness, and neuroticism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".