Alexithymia and Pain in Three Chronic Pain Samples: Comparing Caucasians and African Americans
Bibliographic record
Abstract
OBJECTIVE: African Americans often report greater pain than do Caucasians, but the factors responsible for this discrepancy are not known. We examined whether alexithymia-the trait of difficulty identifying and describing one's feelings and lacking introspection-may contribute to this ethnic group difference. We tested whether the mean level of alexithymia is higher, and whether alexithymia and pain are more highly correlated, among African Americans than among Caucasians in patients with chronic pain disorders. DESIGN: Three cross-sectional, correlational studies were conducted on three separate samples of patients with chronic pain. Analyses examined the full sample and then Caucasians and African Americans separately. SETTING AND PATIENTS: Patients were recruited primarily from treatment settings. Samples were patients with rheumatoid arthritis (N = 155), migraine headaches (N = 160), or systemic lupus erythematosus (N = 123), and each sample included only Caucasians or African Americans. MEASURES: The Toronto Alexithymia Scale-20 assessed global alexithymia and three alexithymia facets. Pain severity, functional disability, or symptoms were also measured on each sample. RESULTS: Similar findings occurred across all three samples. African Americans had only slightly higher mean alexithymia levels than did Caucasians, and this was partly accounted for by socioeconomic differences between groups. More importantly, alexithymia correlated only weakly with pain or symptom severity for each full sample, but the two ethnic groups showed different patterns. Alexithymia correlated positively with pain severity among African Americans, but was uncorrelated with pain among Caucasians, even after covarying for various socioeconomic variables. CONCLUSIONS: Alexithymia is more correlated with pain severity among African Americans with chronic pain disorders than among Caucasians, potentially contributing to the higher pain reports among African Americans.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".