Causal attributions and alexithymia in female patients with fibromyalgia or chronic low back pain
Bibliographic record
Abstract
AIMS: Our general objective was to assess the psychological symptoms and the types of causal attributions linked to the symptoms among women chronic pain. METHODS: 70 fibromyalgia (FM) patients, 56 chronic low back pain (CLBP) patients and 72 healthy controls were assessed within a general hospital setting, using the Toronto Alexithymia Scale, Brief Symptom Inventory and Symptom Interpretation Questionnaire. Three-way analysis of variance and chi-square tests were used for inter-group comparisons, followed by multivariate correlation, covariate analysis and linear regression. RESULTS: Alexithymia, somatization, depression, anxiety and hostility scores were significantly higher in FM patients relative to CLBP patients and healthy controls (P < 0.05). Alexithymia was linked to psychological attributions in FM patients and to somatic attributions in CLBP patients. Psychological attributions, the number of symptoms and difficulty in describing emotions were related to increased anxiety in FM patients. Depression, anxiety and somatization were significantly increased in subjects with high alexithymia scores in the FM group. There was no difference between groups regarding causal attributions. CONCLUSIONS: Causal attributions do not seem to have distinctive features between functional somatic syndromes like FM and CLBP, though differences might exist between groups as to the effects of coexisting psychological distress symptoms like anxiety and depression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".