The Role of Personality Traits, Cognitive Performances, and Perceived Social Support on Job Satisfaction Among Individuals with Chronic Psychiatric Disorders
Bibliographic record
Abstract
Background: This study was conducted due to the importance of job satisfaction in chronic psychiatric patients and the lack of sufficient information about the associated variables. Objectives: The study aimed to assess the role of personality traits, cognitive performances, and perceived social support on job satisfaction among individuals with chronic psychiatric disorders employed in supportive-productive workshops in Semnan. Methods: This cross-sectional study was conducted using a quantitative and descriptive-correlational regression method. It included 152 individuals (both male and female) with chronic psychiatric disorders employed in supportive-productive workshops in Semnan city in 2022. Participants were randomly selected through a lottery method. Data were gathered using the NEO Five-Factor Inventory by Costa and McCrae (1985), the Field and Roth Job Satisfaction questionnaire (1951), Zimet's (1988) scale for perceived social support, and the Montreal Cognitive Assessment (1996). Data analysis was performed using regression analysis with SPSS version 24. Results: The regression analysis illustrated that although neuroticism, agreeableness, extraversion, conscientiousness, perceived social support, and cognitive performances had a significant predictive role on job satisfaction among patients with chronic psychiatric disorders, perceived social support and conscientiousness had the most significant effect, while cognitive performances had the least significant effect on predicting job satisfaction (P < 0.05). Conclusions: It is recommended that authorities pay special attention to the perceived social support from society and family, as well as cognitive rehabilitation focusing on attention and concentration for individuals with chronic psychiatric disorders.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".