Does Experience Influence Judgements of Pain Behaviour? Evidence from Relatives of Pain Patients and Therapists
Bibliographic record
Abstract
OBJECTIVE: Judgments about an individual's pain can be profoundly important to sufferers. Relatively few studies have examined variables that may affect observers' judgments of the pain of others. The present article reports two studies investigating the relationship between different kinds of exposure to pain problems and observers' ratings of the pain intensity of patients. DESIGN: In the first study, 82 observers were classified into groups with positive and negative family histories of chronic pain. They viewed a videotape showing the facial expressions of shoulder pain patients undergoing physiotherapy assessments and rated the pain experienced by the subjects. In the second study, the data from observers having no experience with pain problems were compared with data collected from therapists having considerable experience with pain problems. RESULTS: Observers with a positive family history of chronic pain attributed greater pain to the patients than those with a negative family history of chronic pain. Professionals' pain judgments were lower than those of control subjects. CONCLUSIONS: Together, the findings imply that one's experiences with the different problems of pain patients may affect pain judgments. Alternative interpretations of the findings are considered.
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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 | 0.005 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".