Teaching cognitive and affective empathy in medicine: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
PURPOSE: Empathy can be divided into cognitive empathy (CE) and affective empathy (AE). CE is defined as the accurate understanding and appropriate response to others' thoughts whereas AE is defined as the accurate understanding and appropriate response to others' emotions. The overall purpose of this systematic review and meta-analysis is to assess the effectiveness of empathy interventions in physicians and physicians-in-training in increasing CE and AE. Specifically, we are interested in examining whether specific teaching methods and intervention designs may contribute to greater empathy intervention effectiveness for CE and AE outcomes. METHOD: Studies searched included randomized controlled trials conducted between 1971 to 2022 examining empathy interventions for medical students and physicians. Thirty-six studies, consisting of 3,833 participants, met the inclusion criteria. Data were analysed using random-effects pairwise meta-analysis. RESULTS: = .46 (95% CI = .30, .62)]. Heterogeneity of effects was evident for both analyses. The effectiveness of interventions on AE was moderated by measurement type. Intervention effectiveness was not significantly moderated by other intervention and study characteristics for either type of empathy. CONCLUSIONS: There is evidence of key differences, and similarities, in how CE and AE is taught to medical students and professionals; however, the small number and high level of heterogeneity in studies makes this difficult to confirm. Research in this field will benefit from investigators standardizing teaching and research methods across studies.
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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.022 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".