Investigating the relationship between pain indicators and observers’ judgements of pain
Bibliographic record
Abstract
BACKGROUND: Due to the inherent subjectivity of pain, it is difficult to make accurate judgements of pain in others. Research has found discrepancies between the ways in which perceived "objective" (e.g., medical evidence of injury) and "subjective" information (e.g., self-report) influence judgements of pain. This study aims to explore which potential cues (depictions of sensory input, brain activation, self-reported pain and facial expressions) participants are most influenced by when evaluating pain in others. METHODS: First, 60 participants (23 women, 36 ± 10 years old) judged who was in more pain between two different pain indicators representing two different patients. These trials revealed which congruent indicator (i.e., two high pain indicators) would most influence participant decisions. Second, participants prescribed quantities of analgesia for one patient's pain based on two different pain indicators. These trials revealed which incongruent indicators (i.e., one high and one low indicator) would most influence participant decisions. RESULTS: As predicted, facial expressions were perceived as subjective and were the least likely, among all pain indicators, to influence observer's judgements of pain. Participants relied upon indicators they perceived as objective. Self-report pain ratings had the greatest influence on participants judgements about how much analgesic cream to prescribe and was perceived as objective by half of the participants. CONCLUSIONS: We found that in situations where incongruent information was presented about an individual's pain, participants relied on pain indicators that they perceived to be objective. The current study provides important insights about biases that people hold when making judgements of pain in others. SIGNIFICANCE: Interpretation and assessment of pain remains one of the largest barriers to pain management and involves complex, idiosyncratic processing. This study provides insights into what information participants view as critical in making attributions of pain when presented with multiple, seemingly incongruent sources of information.
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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 | 0.151 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".