Pain Catastrophizing Thoughts Explain the Link Between Perceived Caregiver Responses and Pain Behaviors of Patients With Chronic Musculoskeletal Pain
Bibliographic record
Abstract
PURPOSE: Caregivers' responses to pain behaviors of patients with chronic pain have an essential role in how patients perceive their pain condition. The current study investigated the mediating role of pain catastrophizing on the link between perceived caregiver responses and patient pain behaviors. MATERIALS AND METHODS: The sample of this cross-sectional study consisted of 200 patients with chronic pain (mean of age = 44.6; 71.5% were female). Participants responded to measures assessing their perception of their caregiver responses to their pain, their pain catastrophizing thoughts, and their pain behaviors. RESULTS: The mediation analyses showed that perceived distracting responses were negatively related to pain catastrophizing level in patients, which in turn was positively associated with expressing pain behaviors. Besides, perceived caregiver negative responses were positively associated with catastrophizing thoughts, which in turn was positively related to expressing pain behaviors. CONCLUSION: Patients' perceptions regarding how their caregiver responds to their pain condition can be related to their thoughts about their pain and how they react to their pain situation. Investigating the external sources that might have an impact on patients' reactions to their pain, especially when those external sources are caregivers who, in most situations, are with the patients for a prolonged duration, is essential.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".