Bibliographic record
Abstract
Empathy in medical care has been one of the focal points in the debate over the bright and dark sides of empathy. Whereas physician empathy is sometimes considered necessary for better physician-patient interactions, and is often desired by patients, it also has been described as a potential risk for exhaustion among physicians who must cope with their professional demands of confronting acute and chronic suffering. The present study compared physicians against demographically matched non-physicians on a novel behavioural assessment of empathy, in which they choose between empathizing or remaining detached from suffering targets over a series of trials. Results revealed no statistical differences between physicians and non-physicians in their empathy avoidance, though physicians were descriptively more likely to choose empathy. Additionally, both groups were likely to perceive empathy as cognitively challenging, and perceived cognitive costs of empathy associated with empathy avoidance. Across groups, there were also no statistically significant differences in self-reported trait empathy measures and empathy-related motivations and beliefs. Overall, these results suggest that physicians and non-physicians were more similar than different in terms of their empathic choices and in their assessments of the costs and benefits of empathy for others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".