PERSONALITY TRAITS ARE DIFFERENTLY ASSOCIATED WITH DEPRESSION AND ANXIETY: EVIDENCE FROM APPLYING BIVARIATE MULTIPLE BINARY LOGISTIC REGRESSION ON A LARGE SAMPLE OF GENERAL ADULTS
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to explore the association of five factors personality traits, as predictor variables, with the anxiety and depression as joint dependent variables in an Iranian adult population. SUBJECTS AND METHODS: A total of 3175 subjects living in Isfahan participated in this cross-sectional population-based study (SEPAHAN) and completed self-administered questionnaires about demographic, life style, gastrointestinal disorders, personality traits, social support, and psychological problems. Data was analyzed using bivariate multiple binary logistic regression in R Free statistical software. RESULTS: The results indicated high scores of neuroticisms increase the risk of anxiety and depression after adjustment for the potential confounders in total sample (OR (95% CI): 1.22 (1.19-1.24) and 1.19 (1.17-1.21), respectively) as well as in both male and female. In contrast, joint inverse associations were found between anxiety and depression with high extraversion (OR (95% CI): 0.90 (0.88-0.92) and 0.91 (0.89-0.92), respectively), agreeableness (0.93 (0.91-0.96) and 0.94 (0.92-0.96) respectively) and conscientiousness scores (0.95 (0.93-0.97) and 0.95 (0.94-0.97) respectively) as well as in both male and female. Furthermore, higher scores of openness had significant inverse association with depression in male. CONCLUSION: The present study indicated that higher scores of neuroticism, however lower extraversion, conscientiousness and agreeableness scores are risk factors for both anxiety and depression in Iranian adult population. It is suggested to perform family studies or twin and genetic association studies with considering combinations of personality traits (personality styles), and also measuring personality traits at the facet level.
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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 | 0.001 | 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 | 0.000 | 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 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".