@inproceedings{lahnala-etal-2024-appraisal,
    title = "Appraisal Framework for Clinical Empathy: A Novel Application to Breaking Bad News Conversations",
    author = "Lahnala, Allison Claire  and
      Neuendorf, B{\'e}la  and
      Thomin, Alexander  and
      Welch, Charles  and
      Stibane, Tina  and
      Flek, Lucie",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.124",
    pages = "1393--1407",
    abstract = "Empathy is essential in healthcare communication. We introduce an annotation approach that draws on well-established frameworks for \textit{clinical empathy} and \textit{breaking bad news} (BBN) conversations for considering the interactive dynamics of discourse relations. We construct Empathy in BBNs, a span-relation task dataset of simulated BBN conversations in German, using our annotation scheme, in collaboration with a large medical school to support research on educational tools for medical didactics. The annotation is based on 1) Pounds (2011){'}s appraisal framework for clinical empathy, which is grounded in systemic functional linguistics, and 2) the SPIKES protocol for breaking bad news (Baile et al., 2000), commonly taught in medical didactics training. This approach presents novel opportunities to study clinical empathic behavior and enables the training of models to detect causal relations involving empathy, a highly desirable feature of systems that can provide feedback to medical professionals in training. We present illustrative examples, discuss applications of the annotation scheme, and insights we can draw from the framework.",
}

Empathy is essential in healthcare communication. We introduce an annotation approach that draws on well-established frameworks for \textit{clinical empathy} and \textit{breaking bad news} (BBN) conversations for considering the interactive dynamics of discourse relations. We construct Empathy in BBNs, a span-relation task dataset of simulated BBN conversations in German, using our annotation scheme, in collaboration with a large medical school to support research on educational tools for medical didactics. The annotation is based on 1) Pounds (2011){'}s appraisal framework for clinical empathy, which is grounded in systemic functional linguistics, and 2) the SPIKES protocol for breaking bad news (Baile et al., 2000), commonly taught in medical didactics training. This approach presents novel opportunities to study clinical empathic behavior and enables the training of models to detect causal relations involving empathy, a highly desirable feature of systems that can provide feedback to medical professionals in training. We present illustrative examples, discuss applications of the annotation scheme, and insights we can draw from the framework.
Lahnala, Allison Claire Neuendorf, B{\'e}la Thomin, Alex er Welch, Charles Stibane, Tina Flek, Lucie
Appraisal Framework for Clinical Empathy: A Novel Application to Breaking Bad News Conversations