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Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Assessment of the Gastrointestinal System II: Health Perception Pattern01:29

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Assessing the gastrointestinal (GI) system is a complex process that begins with collecting subjective data. This data, collected through patient interviews, provides crucial insights into the patient's health history, perception patterns, and lifestyle habits, all contributing significantly to GI health.
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Health Information Technology and Healthcare Information System01:30

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Health Information Technology (HIT)
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Patient-centered care involves delivering care beyond inpatient hospitalization. Reflective practice can enhance a patient-centered approach. Reflective practice is a process of reasoning that considers all aspects of the present situation, including practicalities, learning from personal practice, and consideration of patient needs. Patients appreciate care decisions made while considering their input. Involving the patient in their care provides the patient with a sense of contribution rather...
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There are various healthcare agencies in the United States—some of which are managed by religious institutions and others by different government branches.
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SBAR II: Application of SBAR01:14

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SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
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Examining otolaryngologists' attitudes towards large language models (LLMs) such as ChatGPT: a comprehensive deep learning analysis.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery·2023
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Understanding the Perceptions of Healthcare Researchers Regarding ChatGPT: A Study Based on Bidirectional Encoder

S V Praveen1, Vajratiya Vajrobol2

  • 1Department of Analytics, Xavier Institute of Management and Entrepreneurship, Bangalore, India. praveenscissci@gmail.com.

Annals of Biomedical Engineering
|May 2, 2023
PubMed
Summary

Healthcare researchers have mixed emotions about using ChatGPT for research. Deep learning analysis of social media posts reveals both positive and negative sentiments regarding this advanced AI tool.

Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Healthcare Research

Background:

  • The rapid advancement of generative artificial intelligence, exemplified by ChatGPT, has sparked significant debate within the scientific community.
Keywords:
ChatGPTDeep learningHealth care researchNatural language processingPerception

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  • Concerns exist regarding the ethical implications and potential impact of AI tools on the integrity and practice of healthcare research.