Catastrophic thinking and heightened perception of pain in others
Bibliographic record
Abstract
Past research has shown that pain catastrophizing contributes to heightened pain experience. The hypothesis advanced in this study was that individuals who score high on measures of pain catastrophizing would also perceive more intense pain in others. The study also examined the role of pain behaviour as a determinant of the relation between catastrophizing and estimates of others' pain. To test the hypothesis, 60 undergraduates were asked to view videotapes of individuals taking part in a cold pressor procedure. Each individual in the videotapes was shown three times over the course of a 1min immersion such that the same individual was observed experiencing different levels of pain. Correlational analyses revealed a significant positive correlation between levels of pain catastrophizing and inferred pain intensity, r=.31, p<.01. Follow-up analyses indicated that catastrophizing was associated with a heightened propensity to rely on pain behaviour as a basis for drawing inferences about others' pain experience. Catastrophizing was associated with more accurate pain inferences on only one of three indices of inferential accuracy. The pattern of findings suggests that increasing reliance on pain behaviour as a means of inferring others' pain will not necessarily yield more accurate estimates. Discussion addresses the processes that might underlie the propensity to attend more to others' pain behaviour, and the clinical and interpersonal consequences of perceiving more pain in others.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".