Patient Responses to the Term Pain Catastrophizing: Thematic Analysis of Cross-sectional International Data
Bibliographic record
Abstract
Pain catastrophizing is understood as a negative cognitive and emotional response to pain. Researchers, advocates and patients have reported stigmatizing effects of the term in clinical settings and the media. We conducted an international study to investigate patient perspectives on the term pain catastrophizing. Open-ended electronic patient and caregiver proxy surveys were promoted internationally by collaborator stakeholders and through social media. 3,521 surveys were received from 47 countries (77.3% from the U.S.). The sample was mainly female (82.1%), with a mean age of 41.62 (SD 12.03) years; 95% reported ongoing pain and pain duration > 10 years (68.4%). Forty-five percent (n = 1,295) had heard of the term pain catastrophizing; 12% (n = 349) reported being described as a 'pain catastrophizer' by a clinician with associated high levels of feeling blamed, judged, and dismissed. We present qualitative thematic data analytics for responses to open-ended questions, with 32% of responses highlighting the problematic nature of the term. We present the patients' perspective on the term pain catastrophizing, its material effect on clinical experiences, and associations with negative gender stereotypes. Use of patient-centered terminology may be important for favorably shaping the social context of patients' experience of pain and pain care. PERSPECTIVE: Our international patient survey found that 45% had heard of the term pain catastrophizing, about one-third spontaneously rated the term as problematic, and 12% reported the term was applied to them with most stating this was a negative experience. Clinician education on patient-centered terminology may improve care and reduce stigma.
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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 | 0.026 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".