Improving Empathy in the Care of Pain Patients
Bibliographic record
Abstract
Empathy is associated with countless benefits in clinical interactions, yet it is not always optimal in health care providers. Social neuroscience offers a window onto the cerebral processes underlying the complex relationships between the multiple components of empathy, patient care, and the caregiver's well-being. Neuroimaging studies have revealed patterns of empathy-related neural responses that shed some light on the mechanisms that could partially explain the phenomena of empathy decline and pain underestimation in health care providers. Such information, complementary to behavioral research findings, may help develop new means of improving empathy in health care, as long as interpretation of neuroimaging data remains grounded. Additionally, research on empathy in this context has largely focused on how clinicians' empathy may affect patient outcomes, but the relationship between empathy and well-being in health care providers is often neglected. The quest to optimize empathy in patient–clinician interactions must take into account the welfare of both members of this dyad.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".