The Predictive Value of Personality Traits for Psychological Problems (Stress, Anxiety and Depression): Results from a Large Population Based Study
Bibliographic record
Abstract
The current study aimed to determine the prognostic values of personality traits for common psychological problems in a large sample of Iranian adult. In a large sample of healthy people (n = 4763) who lived in Isfahan province; the NEO-FFI was used to assess the personality traits; depression and anxiety were assessed using the "Hospital Anxiety and Depression Scale (HADS)" also stress was measured through Persian validated version of General Health Questionnaire (GHQ-12). Receiver Operating Characteristics Curve (ROC) analysis was used as main statistical method for data analysis. ROC analysis showed neuroticism was the best predictor for all psychological problems with highest area under the curve (AUC) (95% confidence interval) for stress, 0.837 (0.837-0.851), anxiety 0.861 (0.847-0.876) and depression 0.833 (0.820-0.846) (p < .001) and the corresponding cut-off points (sensitivity, specificity), were 21.5 (77%, 66%), 22.5 (81%, 77%) and 20.5 (77%, 74%), respectively. Other personality traits were significant protective factors for being affected with psychological problems (p < .001). Similar findings were observed separately in women and men. The present study showed that the neuroticism is significant risk factor for being affected with three psychological problems while other traits are significant protective factors. Personality traits are useful indices for screening psychological problems and an effective pathway toward prevention in general population.
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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 | 0.009 | 0.002 |
| 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 | 0.000 |
| 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".