Emotion Regulation and Psychological Dependence on Pain Medication among Hospital Outpatients with Chronic Spinal Pain: The Influence of Rumination about Pain and Alexithymia
Bibliographic record
Abstract
Objective: To examine the extent to which pain acceptance, pain catastrophising and alexithymia moderate associations between pain intensity and psychological pain medication dependence. Methods: Participants (106 hospital outpatients with chronic spinal pain) completed the Leeds Dependence Questionnaire (LDQ) to measure psychological dependence on pain medication, and the Chronic Pain Acceptance Questionnaire-8 (CPAQ-8), the Pain Catastrophising Scale (PCS) and the Toronto Alexithymia Scale-20 (TAS-20), plus the Depression, Anxiety and Stress Scale-21 (DASS-21). Results: Multiple linear regression showed that degree of psychological dependence (measured dimensionally across the range of LDQ scores) was associated with TAS subscale difficulty identifying feelings (DIF) (β = 0.249, p = <0.002) and PCS subscale rumination (β = 0.193, p = 0.030), independently of pain intensity and risk behaviors for medication misuse. The effect of pain intensity was moderated by rumination, with pain intensity more strongly associated with dependence when rumination was high (interaction β = 0.192, p = 0.004). Logistic regression showed that the effect of pain intensity on severe dependence (measured categorically as LDQ score ≥ 20) was moderated by alexithymia, so that severe dependence was independently associated with the combination of intense pain and high alexithymia (interaction odds ratio = 7.26, 95% CIs = 1.63–32.42, p = 0.009). Conclusions: Rumination and alexithymia moderated the associations between pain intensity and psychological pain medication dependence, consistent with emotion regulation theory. This raises the possibility that specifically targeting rumination about pain and symptoms of alexithymia could potentially improve the effectiveness of psychological interventions for chronic pain and help people to avoid or reduce their psychological dependence on pain medication.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".