A systematic review of the associations between empathy measures and patient outcomes in cancer care
Bibliographic record
Abstract
OBJECTIVE: Despite a call for empathy in medical settings, little is known about the effects of the empathy of health care professionals on patient outcomes. This review investigates the links between physicians' or nurses' empathy and patient outcomes in oncology. METHOD: With the use of multiple databases, a systematic search was performed using a combination of terms and subject headings of empathy or perspective taking or clinician-patient communication, oncology or end-of-life setting and physicians or nurses. Among the 394 hits returned, 39 studies met the inclusion criteria of a quantitative measure of empathy or empathy-related constructs linked to patient outcomes. RESULTS: Empathy was mainly evaluated using patient self-reports and verbal interaction coding. Investigated outcomes were mainly proximal patient satisfaction and psychological adjustment. Clinicians' empathy was related to higher patient satisfaction and lower distress in retrospective studies and when the measure was patient-reported. Coding systems yielded divergent conclusions. Empathy was not related to patient empowerment (e.g. medical knowledge, coping). CONCLUSION: Overall, clinicians' empathy has beneficial effects according to patient perceptions. However, in order to disentangle components of the benefits of empathy and provide professionals with concrete advice, future research should apply different empathy assessment approaches simultaneously, including a perspective-taking task on patients' expectations and needs at precise moments. Indeed, clinicians' understanding of patients' perspectives is the core component of medical empathy, but it is often assessed only from the patient's point of view. Clinicians' evaluations of patients' perspectives should be studied and compared with patients' reports so that problematic gaps between the two perspectives can be addressed.
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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 | 0.014 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.019 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".