Personality, Defense Mechanisms and Psychological Distress in Women with Fibromyalgia
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown that many personality traits are associated with fibromyalgia (FM), worsening both the quality of life and psychological distress of patients. Despite the high comorbidity of psychopathological disorders in this syndrome and their association with immature defense styles, few studies have examined the defense mechanisms used by FM patients. The main aim of our study was to investigate personality traits and defense mechanisms in FM patients compared to in a healthy control group (HC). Moreover, we investigated the effect of personality traits and defense mechanisms on psychological distress in both FM and HC groups. METHODS: A total of 54 women with FM and 54 healthy women completed the (1) Temperament and Character Inventory-Revised; (2) the Toronto Alexithymia Scale; (3) the Defense Style Questionnaire; and (4) the Hospital Anxiety and Depression Scale. RESULTS: The results indicated that FM patients display higher alexithymia, higher harm avoidance, lower self-directedness, lower persistence, and the higher use of a maladaptive defense style compared to HC. We found that alexithymia, harm avoidance, and maladaptive defense style are significant predictors of patients' psychological distress. Moreover, harm avoidance and adaptive defense style significantly predicted psychological distress in the HC group. CONCLUSION: The present study is the first to explore the contribution of both defense mechanisms and personality characteristics on the psychological distress of FM patients. Our findings have important clinical implications and may help diagnose and treat FM patients more in depth.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".