Alexithymia in Family Caregivers of Advanced Cancer Patients Is Associated with High Personalized Pain Goal Scores: A Pilot Study
Bibliographic record
Abstract
Background: Alexithymia, or difficulty identifying and describing emotions and sensations, contributes to an increased risk of chronic pain, and low help-seeking. Objective: To investigate whether family caregivers of advanced cancer patients visiting a palliative care department had alexithymia, and whether this was related to their pain intensity, personalized pain goals, and help-seeking for chronic musculoskeletal pain. Design: A single-center cross-sectional survey. Measurements: Pain intensity was evaluated using a numerical rating scale. Pain improvement was evaluated against personal goals. Alexithymia was assessed using the Toronto Alexithymia Scale-20 (TAS-20), and anxiety and depression using the Hospital Anxiety and Depression Scale. Setting/Subjects: Of 320 family caregivers visiting the palliative care department, 152 (47.5%) had chronic musculoskeletal pain; all 152 were included in the study. Results: Alexithymia was observed in 36.2% of participants. Participants with higher scores on the TAS-20 tended to have higher pain intensity scores and personal pain goal scores. TAS-20 score had the strongest correlation with personal pain goals, with a correlation coefficient of 0.555 (p < 0.001). Conclusions: Pain intensity in family caregivers with alexithymia tended to be high. These participants set higher personal pain goals (lower goals for symptom improvement) than those without alexithymia. We found no difference in personal pain goal response between family caregivers with and without alexithymia. When we examine pain in family members with alexithymia who are caring for cancer patients, we need to recognize that they may set higher personal pain goals and seek less help.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".