Expression of Pain Behaviors and Perceived Partner Responses in Individuals With Chronic Pain
Bibliographic record
Abstract
OBJECTIVE: Expressions of pain by individuals with chronic pain may encourage solicitous and distracting responses from some partners and punishing responses from others. Partners' responses can impact the well-being of individuals with chronic pain. Yet information about factors that can explain the link between expression of pain behaviors and different partners' responses is scarce. The objective of this study was to investigate the role of perceived partner burden and relationship quality in the link between expressions of pain behaviors and perceived partner responses (ie, solicitous, distracting, and punishing responses). MATERIALS AND METHODS: Participants were 158 individuals with chronic pain (ie, experiencing pain on most days for at least 6 months before participating in the study) who completed questionnaires about pain behaviors, as well as perceptions of partner burden, relationship quality, and partners' solicitous, distracting, and punishing responses. The link between expressing pain and each type of partner response was investigated by serial mediation analysis. Partner burden and relationship quality were entered into all analyses as the first and the second mediator, respectively. RESULTS: Expressing more pain was related to higher levels of perceived partner burden, which in turn, was associated with poorer relationship quality. Poorer relationship quality was associated with reporting fewer solicitous and distracting partner responses and more punishing responses. DISCUSSION: Enhanced partner burden and reduced relationship quality may be one pathway through which pain behaviors relate to partner responses.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".