Psychological Defense Mechanisms and Alexithymia in Cancer Patients
Bibliographic record
Abstract
Introduction: The goal of this research was to compare Alexithymia and psychological defense mechanisms in cancer patients with normal group. Also, investigation of the predictive role of three defense mechanisms in Alexithymia was considered. Materials and Methods: From chemotherapy ward of Shahid Beheshti hospital of Qom city, 45 cancer patients were selected by convenient sampling method. Also, 45 employees of this hospital were included as the normal group. Defense Mechanisms Questionnaire and Toronto Alexithymia Scale-20 were used. Data was analyzed with Independent Sample T-test, Pearson Correlation and Multivariate Regression. Results: Compared with normal people, cancerous patients had higher scores in alexithymia (p=0.01), difficulty in emotions recognition subscale (p=0.03) and non-developed defense mechanisms (p=0.007). Non-developed defense mechanisms had significant relationships with alexithymia and difficulty in emotion’s recognition and description subscales (p>0.01), also non-developed defense mechanisms could predict alexithymia in cancerous patients (p>0.005). Conclusions: Findings indicates that cancer is a stressful event that can cause non-developed defense mechanisms start to emerge as dominant psychological defense mechanisms in the majority of patients. Alexithymia which has a relation with defense mechanisms is also a dynamic reaction for coping with unpleasant emotions driven by the illness.
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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 | 0.001 |
| 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) | 0.002 | 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".