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相关概念视频

Therapeutic Communication01:30

Therapeutic Communication

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Communication is a lifelong learning process. Through therapeutic communication, nurses can collect relevant assessment data, provide education and counseling, and interact during nursing interventions. Sending and receiving messages occur through verbal and nonverbal communication techniques and can happen separately or simultaneously.
Verbal communication depends on language or a prescribed way of using words so that people can share information effectively. The critical aspects of verbal...
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Role of Communication in the Nursing Process I: Assessment and Diagnosis01:25

Role of Communication in the Nursing Process I: Assessment and Diagnosis

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The nursing process uses scientific reasoning, problem-solving, and critical thinking to guide nurses in providing patients with appropriate care. This process is a systematic approach to recognize, avoid, and treat current or potential health issues while promoting the patient's well-being.
The nursing process considers the patient's emotional and physical well-being. The process can be repeated or stopped at any point if judged essential. Assessment is the first step in the nursing...
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Interdisciplinary Care: The Health Care Team-II01:18

Interdisciplinary Care: The Health Care Team-II

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An interdisciplinary team includes many healthcare professionals working together and utilizing their skills, knowledge, and expertise to provide holistic and quality patient care. Here are a few more healthcare professionals.
Physical Therapist
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Effective communication among healthcare professionals during hand-off reporting is essential to delivering safe and continuous patient care. Common professional interactions include reports to healthcare team members, hand-off, and transfer reports. Nurses routinely report information to other healthcare team members and also urgently contact healthcare providers to report changes in patient status.
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Modeling in Therapy01:26

Modeling in Therapy

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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
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Impression Management Techniques IV: Altercasting01:14

Impression Management Techniques IV: Altercasting

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Altercasting is a strategic communication technique in which an individual imposes a specific identity or social role onto another person to influence their behavior and shape the interaction. By presuming a role—such as “responsible leader” or “patient person”—altercasting encourages the target to conform to that identity, often aligning their behavior with the expectations associated with the role. The power of this tactic lies in its subtlety; once a role...
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相关实验视频

Updated: Feb 28, 2026

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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在临床对话中识别演讲者的角色

Andrew Zolensky1, Kuk Jin Jang2, Janice Sabin3

  • 1Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA, Andrew.Zolensky@PennMedicine.upenn.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
PubMed
概括
此摘要是机器生成的。

大型语言模型 (LLM) 现在可以自动识别临床对话中的说话角色. 这项技术通过准确区分医生,患者和其他护理人员来改善患者-临床医生沟通分析.

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科学领域:

  • 医疗信息学 医疗信息学
  • 计算语言学 计算语言学
  • 医疗保健中的人工智能

背景情况:

  • 患者与临床医生的沟通对于理解医疗保健相互作用和结果至关重要.
  • 手动分析临床话语是耗时且具有挑战性的,特别是在演讲者角色识别 (SRI) 方面.
  • 现有的自动语音识别系统与日记不具备将特定角色分配给演讲者的能力.

研究的目的:

  • 调查大型语言模型 (LLM) 在临床环境中用于演讲者角色识别 (SRI) 的有效性.
  • 用单独的语言特征来评估SRI性能,而不是使用日记化标识符.
  • 评估识别器腐败对基于LLM的SRI准确性的影响.

主要方法:

  • 利用BERT,一个大型语言模型,用于临床成绩单上的演讲者角色识别 (SRI).
  • 采用垂直转折细分和日记化标识符.
  • 微调了BERT模型,使用不同级别的标识符腐败来测试性能稳定性.

主要成果:

  • 通过仅使用语言信号,BERT实现了82%的准确性和82%的SRIF1得分.
  • 整合精确的日记识别器显著提高了性能,达到95%的准确性和95%的F1分数.
  • 该研究表明,LLM在临床环境中具有SRI的能力.

结论:

  • 精心调整的大型语言模型 (LLM) 在临床成绩单中对自动发言者角色识别 (SRI) 非常有效.
  • 法律法规提供了一个强大的解决方案,用于分析患者和临床医生的沟通动态.
  • 将LLM与准确的日记记录相结合,进一步提高了SRI在医疗保健环境中的精度.