Catastrophizers with Chronic Pain Display more Pain Behaviour when in a Relationship with a Low Catastrophizing Spouse
Bibliographic record
Abstract
The present study examined the relationship between couple concordance of catastrophizing and adverse pain outcomes. Possible mechanisms underlying the relationship between couple concordance of catastrophizing and pain outcomes were also explored. Fifty-eight couples were recruited for the study. The chronic pain patients were filmed while lifting a series of weighted canisters. The spouse was later invited to view the video and answer questions about the pain experience of their partner. Median splits on Pain Catastrophizing Scale scores were used to create four 'catastrophizing concordance' groups: low catastrophizing patient-low catastrophizing spouse; low catastrophizing patient-high catastrophizing spouse; high catastrophizing patient-low catastrophizing spouse; and high catastrophizing patient-high catastrophizing spouse. Analyses revealed that high catastrophizing pain patients who were in a relationship with a low catastrophizing spouse displayed more pain behaviours than patients in all other groups. These findings suggest that high catastrophizing chronic pain patients may need to increase the 'volume' of pain communication to compensate for low catastrophizing spouses' tendency to underestimate the severity of their pain experience. Patients' perceived solicitousness and punitive response from the spouse could not explain the group differences in pain behaviour. Theoretical and clinical implications of the findings are discussed.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".