Contextual determinants of pain judgments
Bibliographic record
Abstract
The objective of this study was to examine the influence of variations in contextual features of a physically demanding lifting task on the judgments of others' pain. Healthy undergraduates (n=98) were asked to estimate the pain experience of chronic pain patients who were filmed while lifting canisters at different distances from their body. Of interest was whether contextual information (i.e., lifting posture) contributed to pain estimates beyond the variance accounted for by pain behavior. Results indicated that the judgments of others' pain varied significantly as a function of the contextual features of the pain-eliciting task; observers estimated significantly more pain when watching patients lifting canisters positioned further away from the body than canisters closest from the body. Canister position contributed significant unique variance to the prediction of pain estimates even after controlling for observers' use of pain behavior as a basis of pain estimates. Correlational analyses revealed that greater use of the contextual features when judging others' pain was related to a lower discrepancy (higher accuracy) between estimated and self-reported pain ratings. Results also indicated that observers' level of catastrophizing was associated with more accurate pain estimates. The results of a regression analysis further showed that observers' level of catastrophizing contributed to the prediction of the accuracy of pain estimates over and above the variance accounted for by the utilisation of contextual features. Discussion addresses the processes that might underlie the utilisation of contextual features of a pain-eliciting task when estimating others' pain.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".