THE ROLE OF DENIAL AND ALEXITHYMIA ON PAIN PERCEPTION IN WOMEN WITH BREAST CANCER
Bibliographic record
Abstract
Breast cancer is one of the most common and serious problems in women health in which pain is one of the most obvious manifestations that is influenced by many internal and external factors. Hence, the current study was conducted to determine the role of denial and Alexithymia on pain perception in women with breast cancer. Materials & Methods: This correlational study was conducted on all women with breast cancer who referred to the clinical centers of Namazi Hospital in Shiraz at the second half of 2014. Considering the inclusion criteria like subjects’ age between 18 to 60 and the guidance school education as the minimum level of education, a number of 50 patients with breast cancer were selected by the method of purposive sampling method. The subjects were asked to respond to cancer denial interview, fill out the questionnaires of Alexithymia, and the visual assessment scale of pain individually. The collected data were analyzed by tests of Pearson correlation coefficients and multistage regression. Results: The findings showed that pain is negatively and significantly correlated with denial (P<0/001, r=-0/52) and positively and significantly correlated with Alexithymia (P<0/01, r=0/40) and difficulty in identifying feeling (component of Alexithymia) (P<0/05, r=0/30). Regression analysis results also showed that 43% of the whole variance of pain can be explained by denial and Alexithymia. Conclusion: The results showed the role of denial and Alexithymia on the level of pain perception in women with breast cancer which expresses the importance of psychological factors on the level of pain perception in these patients.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".