When you dislike patients, pain is taken less seriously
Bibliographic record
Abstract
This study examined the influence of patients' likability on pain estimations made by observers. Patients' likability was manipulated by means of an evaluative conditioning procedure: pictures of patients were combined with either positive, neutral, or negative personal traits. Next, videos of the patients were presented to 40 observers who rated the pain. Patients were expressing no, mild-, or high-intensity pain. Results indicated lower pain estimations as well as lower perceptual sensitivity toward pain (i.e., lower ability to discriminate between varying levels of pain expression) with regard to patients who were associated with negative personal traits. The effect on pain estimations was only found with regard to patients expressing high-intensity pain. There was no effect on response bias (i.e., the overall tendency to indicate pain). These findings suggest that we take the pain of patients we do not like less seriously than the pain of patients we like.
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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 | 0.004 |
| 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) | 0.001 | 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".