A systematic review identifying effective teaching methods and their combinations for increasing empathy in physicians: pairwise and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Demonstrating empathy is fundamental for providing patient-centered care, however, what components are most effective is not known. Thus, the aim of this study was to identify key teaching methods and intervention characteristics for increasing empathy skills across medical training. METHOD: This study was part of a larger systematic review, which was pre-registered: CRD42018100100. We performed a systematic review, pairwise meta-analysis (PMA) and network meta-analysis (NMA). A systematic search was used across PsycINFO, Medline, CINAHL, Social Work Abstracts, ERIC, ABI/INFORM, and the Cochrane Central Register of Controlled Trials from the inception of the respective databases to October 9, 2022. Studies included randomized controlled trials (RCTs) examining behavioural interventions which targeted empathy skills for physicians and medical students. Studies were excluded if reported summary data could not be converted to an effect size; if the author were unable to be contacted; and if the study did not compare substantively different intervention combinations. Risk of bias was assessed using the Cochrane risk-of-bias tool. Data were pooled using random-effects PMA and NMA. RESULTS: 308 full-text studies were found, of which 111 met the inclusion criteria, totalling 11,111 participants. Overall, a medium effect of interventions was found [d = 0.50 (95% CI = 0.40, 0.60)], meaning the empathy skills of participants improved moderately compared to those in control groups. Publication bias was evident and heterogeneity was high (I2 = 79.19, p < .001). Subgroup analyses of the PMA revealed the following moderators were statistically significant: teaching method, intervention formats, control group; measurement type, and number of teaching methods used. The consistency assumption was met [χ2 [ (33)= 39.11, p = .21] for the NMA. The NMA revealed that didactic and rehearsal were most frequently included among the most effective teaching method combinations. CONCLUSIONS: By using PMA and NMA, we provide novel insights on effective intervention components for improving empathy in medicine. To improve aggregation of evidence, transparent and standardized reporting from studies may help reduce heterogeneity. Overall, our results support the notion that interventions need not be expensive nor prolonged to be effective.
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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 | 0.041 | 0.107 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.057 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".