How Personality Affects the Way Individuals Experience and Interpret Physical Symptoms?
Bibliographic record
Abstract
This study aimed to explore how personality traits influence individuals' experiences and interpretations of physical symptoms. This qualitative study employed semi-structured interviews with 29 participants recruited through online platforms. Theoretical saturation guided data collection, ensuring comprehensive exploration of symptom perception variations. Interviews were transcribed verbatim and analyzed using thematic analysis with NVivo software. The study identified key themes related to emotional responses, cognitive interpretations, behavioral tendencies, and the role of personality traits in shaping symptom perception. The results indicated that neuroticism was associated with heightened symptom sensitivity, emotional amplification, and increased health anxiety, whereas conscientious individuals exhibited structured symptom monitoring and proactive health behaviors. Extraverted participants were more likely to express symptoms openly and seek social reassurance, while introverted individuals internalized their distress and engaged in self-directed coping. Cognitive patterns varied, with some individuals engaging in logical symptom analysis while others exhibited anxiety-driven health rumination and negative interpretation biases. Stress and psychological distress reinforced symptom perception, contributing to a bidirectional relationship between emotional states and physical symptoms. Behavioral responses to symptoms included frequent medical reassurance-seeking, avoidance behaviors, and reliance on alternative health approaches, demonstrating the diverse ways personality shapes health-related decision-making. The findings highlight the critical role of personality in shaping symptom experiences and interpretation, underscoring the need for personalized healthcare approaches. Understanding personality-related differences in symptom perception can inform tailored interventions that address cognitive biases, emotional responses, and behavioral tendencies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".