Alexithymia and Chronic Pain
Bibliographic record
Abstract
OBJECTIVES: Alexithymia has been shown to be associated with key pain-related variables in persons with chronic pain from western countries, but the generalizability of these findings across cultures has not been examined adequately. Also, there remain questions regarding the importance of alexithymia to patient functioning over and above the effects of the general negative affectivity. METHODS: Alexithymia, pain intensity, pain interference, depression, anxiety, and pain catastrophizing were measured in 128 Japanese patients with chronic pain. Because of the low internal consistency coefficients for 2 of the alexithymia scales (measuring difficulty describing feelings and externally oriented feelings) in our sample, we limited our analyses to a scale assessing difficulty identifying feelings and the total alexithymia scale score. RESULTS: Although the 20-item Toronto Alexithymia Scale total and the Difficulty Identifying Feelings scale scores were not significantly associated with pain intensity, these scales were associated with pain interference, catastrophizing, and negative affectivity in our sample. However, these associations became nonsignificant when measures of negative affectivity were controlled. DISCUSSION: The findings support the cross-cultural generalizability of significant associations between alexithymia and both pain interference and catastrophizing. However, whether (1) alexithymia influences patient functioning indirectly by its effects on negative affect or (2) the univariate associations found between alexithymia and measures of patient functioning are a byproduct of both being influenced by negative affect needs to be tested using longitudinal and experimental research.
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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 | 0.003 |
| 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".