Observer trait anxiety is associated with response bias to patient facial pain expression independent of pain catastrophizing
Bibliographic record
Abstract
Nonverbal communication, such as facial expression, is an important component of the communication of pain to an observer. One factor that influences pain perception by an observer is characteristics specific to the observer themselves (ie, ‘top‐down’ characteristics). The authors of this article aimed to assess how anxiety in the observer affects their ability to rate a sufferer’s pain, controlling for pain catastrophizing. BACKGROUND: Top‐down characteristics of an observer influence the detection and estimation of a sufferer’s pain. A comprehensive understanding of these characteristics is important because they influence observer helping behaviours and the sufferer’s experience of pain. OBJECTIVES: To examine the hypothesis that individuals who score high in trait anxiety would perceive more intense pain in others, as indicated by a larger negative response bias, and that this association would persist after adjusting for pain catastrophizing. METHODS: Healthy young adult participants (n=99; 50 male) watched videos containing excerpts of facial expressions taken from patients with shoulder pain and were asked to rate how much pain the patient was experiencing using an 11‐point numerical rating scale. Sensitivity and response bias were calculated using signal detection methods. RESULTS: Trait anxiety was a predictor of response bias after statistically adjusting for pain catastrophizing and observer sex. More anxious individuals had a proclivity toward imputing greater pain to a sufferer. CONCLUSIONS: Individuals scoring higher on trait anxiety were more likely to impute pain to a sufferer. Anxious caregivers may be better able to respond with appropriate intervention once pain behaviour is detected, or they may exacerbate symptoms by engaging in excessive palliative care and solicitous behaviour.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".