Contextual influences on pain communication in couples with and without a partner with chronic pain
Bibliographic record
Abstract
This is an experimental study of pain communication in couples. Despite evidence that chronic pain in one partner impacts both members of the dyad, dyadic influences on pain communication have not been sufficiently examined and are typically studied based on retrospective reports. Our goal was to directly study contextual influences (ie, presence of chronic pain, gender, relationship quality, and pain catastrophizing) on self-reported and nonverbal (ie, facial expressions) pain responses. Couples with (n = 66) and without (n = 65) an individual with chronic pain (ICP) completed relationship and pain catastrophizing questionnaires. Subsequently, one partner underwent a pain task (pain target, PT), while the other partner observed (pain observer, PO). In couples with an ICP, the ICP was assigned to be the PT. Pain intensity and PO perceived pain intensity ratings were recorded at multiple intervals. Facial expressions were video recorded throughout the pain task. Pain-related facial expression was quantified using the Facial Action Coding System. The most consistent predictor of either partner's pain-related facial expression was the pain-related facial expression of the other partner. Pain targets provided higher pain ratings than POs and female PTs reported and showed more pain, regardless of chronic pain status. Gender and the interaction between gender and relationship satisfaction were predictors of pain-related facial expression among PTs, but not POs. None of the examined variables predicted self-reported pain. Results suggest that contextual variables influence pain communication in couples, with distinct influences for PTs and POs. Moreover, self-report and nonverbal responses are not displayed in a parallel manner.
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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 | 0.001 | 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) | 0.002 | 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".