Observers’ impression of the person in pain influences their pain estimation and tendency to help
Bibliographic record
Abstract
BACKGROUND: Estimation of a patient's pain may have a considerable impact on the level of care that patient receives. Many studies have shown that contextual factors may influence an observer's pain estimation. Here, we investigate the effect of an observer's impression of a person in pain and justification of his/her pain on the observer's pain estimation, tendency to help and perceived empathy. METHODS: Thirty healthy individuals (half females) read scenarios aimed to manipulate the reader's impression of characters who ultimately were fired from their work (four positive characters and four negative; half females). Then they observed 1-s videos of four levels of pain expression (neutral, mild, moderate, strong) in those characters during an examination. Subsequently, they rated pain estimation, tendency to help and perceived empathy. Afterwards, they rated their likability of characters and how just they find the end of story. RESULTS: People rated pain in positive characters higher than the pain in negative characters. They also expressed more tendency to help and a higher level of perceived empathy towards positive characters than negative characters. For the highest level of pain in positive characters, perceived injustice towards that person was the best predictor of the observer's pain estimation, tendency to help and perceived empathy. For negative characters, dislikeability was the best predictor of tendency to help and perceived empathy. Justification of their pain was a predictor of pain estimation and tendency to help. CONCLUSION: Observers used different information to evaluate pain in positive and negative individuals. SIGNIFICANCE: Observers' estimation of pain, perceived empathy and tendency to help biases by their understanding of the characteristics of the person in pain. In clinical settings, these biases may influence the quality of care and well-being of patients. Understanding the underlying mechanisms of these biases can help us improve the quality of care and control the effect of prejudices on pain assessment.
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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 | 0.014 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".