A Call for Compassionate Empathy: Analysis of Verbal Empathic Communication between Veterinary Students and Veterinary Clients and their Dogs
Bibliographic record
Abstract
In human medicine, empathy contributes to enhanced patient satisfaction and trust, decreased litigation, and increased adherence to medical recommendations. Understanding client perspectives is crucial in the empathic process; failure to explore these perspectives has been linked to decreased client satisfaction in veterinary consultations. This article explores how veterinary clients verbally expressed emotional concerns during consultations and how veterinary students addressed them. The “Model of Empathic Communication in the Medical Interview” by Suchman et al., is the starting point for a thematic analysis of consultation transcripts. Clients expressed multiple emotional concerns both directly by using explicit words (coded as empathic opportunities-EO), and indirectly (coded as potential empathic opportunities-PEO), throughout the consultations. Indirect examples prevailed and included stories about previous experiences with pet illnesses and pet care received elsewhere. Clients used explicit words, including “fear” and “panic.” Students usually responded with a biomedical focus, including asking medical questions and giving medical explanations. Although students demonstrated various communication skills, they failed to demonstrate a complete verbal compassionate empathic response (a novel code) that includes exploring and verbalizing accurate understanding of the clients’ perspectives and offering help based on this understanding. These findings suggest that strategies to teach compassionate empathy and support its use in the clinical setting are not fully effective, and veterinary students risk entering practice unprepared to employ this vital competency. The authors also introduce an operational definition for compassionate empathy.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".