Nonverbal behaviors perceived as most empathic in a simulated medical context
Bibliographic record
Abstract
Perceiving empathy from healthcare professionals contributes to clinical benefits. Yet, for methodological and ethical reasons, the factors affecting perceived empathy, such as how the nonverbal behaviors of professionals interact, are less understood than those influencing the actual act of empathizing. Two online studies examined how the perception of empathy in a medical context of pain was influenced by factors related to digital healthcare professionals (DHPs) and participants acting as suffering patients. In Study 1 (n = 123), participants watched videos of DHPs showing variations in gaze direction, posture, and facial expression to rate perceived empathy from a visual patient perspective. They perceived more empathy from the face expressing pain, regardless of gaze, posture, and gender of the DHPs. The sex of participants also modulated perceived empathy. Study 2 (n = 116) expanded Study 1 by adding faces expressing pain and sadness of varying intensities, along with perspective-taking instructions, to determine whether higher perceived empathy for the face expressing pain stems from its congruence with the medical pain context. Participants perceived more empathy in faces expressing sadness than pain. Sadness and pain interacted differently with the effects of intensity, posture, and gaze direction. This work challenges the idea that exact congruence with the patient’s affective state is necessary and further contributes to investigating nonverbal behaviors of empathy.
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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 | 0.001 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".