Pain Intensity Is Not Always Associated with Poorer Health Status: Exploring the Moderating Role of Spouse Personality
Bibliographic record
Abstract
Background: Past decades have seen a surge of studies investigating the role of spouses in chronic illness. The present study explored an interpersonal model of health-related quality of life in chronic pain settings. Spouse personality was tested as a moderator of pain intensity-to-health associations in patients with chronic pain. Methods: This is a cross-sectional study. Participants were 185 noncancer chronic pain patients and their spouses. Patients were mostly females (58.4%). Mean age was approximately 56 years for patients and spouses. Patients completed a measure of pain intensity, health-related quality of life, and personality. Spouses also reported on their personality characteristics. Spouse personality was used as the moderator in the relationship between patients' pain intensity and health status. Patient personality was used as a covariate in the moderation analyses. Results: Spouse neuroticism moderated the relationship between pain intensity and physical health status, while spouse introversion moderated the pain-to-mental health association. Conclusions: Results support the idea that the relationship between a chronic stressor, namely, chronic pain, and health-related quality of life may be complex and contextually determined by spousal characteristics. Clinical implications are discussed in the context of couples.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".