Why do Patients Engage in Pain Behaviors? A Qualitative Study Examining the Perspective of Patients and Partners
Bibliographic record
Abstract
OBJECTIVES: Patients' pain behavior plays an important role in the interaction between patients and their partners, as acknowledged in operant models of pain. However, despite the considerable research attention to pain behaviors, the underlying motives of such behaviors are still unclear. The current study explores the motives to engage in pain behaviors and the possible discrepancies between individuals experiencing pain and partners' perceptions of those motives. METHODS: A qualitative study was performed, comprising semistructured interviews with 27 patients with chronic low back pain and their partners. They were recruited through purposive sampling at 2 pain clinics located in Tehran, Iran. RESULTS: Patients and partners mentioned a variety of motives for pain behaviors, including protecting oneself against more pain, regulating negative emotions, informing others about the pain severity, seeking validation or intimacy, gaining advantages from pain, and expressing anger. Patients and partners revealed the most similarities in motives such as protecting oneself against more pain and informing others about the pain severity. However, partners rarely acknowledged patients' motives for seeking validation and they were more likely to mention negative motives (eg, expressing anger). DISCUSSION: In conclusion, partners are more likely to attribute negative motives to the patient's pain behaviors, which may lead to their hostility toward patients. The findings of this study provide new insights into motives of pain behaviors from the perspective of patients and partners, which can inform couple-based interventions in terms of effective pain communication.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".