Empathy training via Kalamazoo Consensus in remote and in-person medical communication: A randomized controlled trial
Why this work is in the frame
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Bibliographic record
Abstract
Background Empathy is crucial in healthcare, facilitating effective communication and improving patient outcomes. Objective This study aimed to evaluate the impact of tele-conference training based on the Kalamazoo Consensus Statement (KCS) on the empathy scores of newly hired physicians in a tele-visit simulation course. Methods From September 2021 to April 2022, we conducted a randomized controlled trial involving 129 medical doctors from 13 hospitals in north-western Tuscany, with an age range of 31 to 42 years. Partecipants were randomly assigned to a trained group (TG) or a control group (CG). Both groups completed the Toronto Empathy Questionnaire (TEQ) and the Balanced Emotional Empathy Scale (BEES) before (T0) and after (T1) the training. The TG underwent a 12-h online communication training course. The CG only completed the questionnaires without further intervention. Results Total sample included 129 partecipants. Results indicated a significant increase in TEQ scores for the TG (55,8 % of total sample; T0: 65.32; T1: 66.42, p = 0.032) and BEES scores (T0: 122.39; T1: 127.50, p = 0.000). The CG (44,2 %) experienced a decrease in TEQ scores (T0: 65.58; T1: 63.75, p = 0.000) but stable BEES scores (T0: 122.16; T1: 120.67, p = 0.317). Female participants consistently exhibited higher empathy scores than males, with training significantly enhancing scores for both genders. Conclusions The tele-conference training effectively improved empathy scores among newly hired physicians. We recommend the implementation of KCS-based training to enhance empathy and communication skills in medical practice. Innovation The pandemic has accelerated the use of tele-education and telemedicine, though opinions on their effectiveness remain divided. However, studies show that empathy can be enhanced through interactive online training, which offers significant innovations for both healthcare professionals' learning and patient care.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it