Survey of Moderating Role of Positive and Negative Affections on the Relationship between Alexithymia and Experience of Pain in Chronic Pain Patients
Bibliographic record
Abstract
Focusing on the somatic sensations is one of the main characteristics of alexithymia. Alexithymic individuals tend to misinterpret these sensa-tions as signs of physical illness. A number of studies have shown that patients with chronic pain express their feelings through physical signs. Positive and negative affects as principal dimensions of emotional expe-riences are among emotions related to pain. The aim of this study was to investigate the moderating role of positive and negative affections on the relationship between alexithymia and experience of pain in chronic pain patients. One hundred chronic musculoskeletal pain patients (67 women, 33 men) participated in this research. All participants were asked to complete the Toronto Alexithymia Scale (TAS-20), the Positive and Negative Affect Schedule (PANAS), and Visual Analogue Scale for Pain Severity (VAS). The results showed that positive and negative af-fects moderated the relationship between alexithymia and experience of pain in opposite directions. Positive affection decreased the effect of alexithymia on experience of pain, while negative affect increased the effect of alexithymia on experience of pain. It can be concluded that the relationship between alexithymia and experience of pain in chronic pain patients was influenced by positive and negative affections.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".