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Related Concept Videos

SBAR II: Application of SBAR01:14

SBAR II: Application of SBAR

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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
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...
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Stereotype Content Model02:16

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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SBAR I: Understanding the Concept01:29

SBAR I: Understanding the Concept

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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.
Standardized methods of communication have been developed to ensure that information is...
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Fundamental Attribution Error01:14

Fundamental Attribution Error

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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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Empathy02:34

Empathy

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Some researchers suggest that altruism operates on empathy. Empathy is the capacity to understand another person’s perspective, to feel what he or she feels. An empathetic person makes an emotional connection with others and feels compelled to help (Batson, 1991). Empathy can be expressed in several ways, including cognitive, affective, and motor. 
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Traits, Mood, and Subjective Wellbeing01:22

Traits, Mood, and Subjective Wellbeing

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Subjective well-being (SWB) refers to an individual's self-evaluation of their overall life satisfaction, happiness, and fulfillment. This multifaceted construct is typically assessed by analyzing the balance of positive and negative emotions alongside perceptions of life satisfaction. Personality traits such as neuroticism and extraversion are strongly associated with variations in SWB, offering critical insights into the underlying mechanisms of emotional well-being.
Neuroticism and...
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Related Experiment Video

Updated: Sep 5, 2025

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

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A BERT-Based Aspect-Level Sentiment Analysis Algorithm for Cross-Domain Text.

Ning Liu1,2, Jianhua Zhao2,3

  • 1College of Economics Management, Shangluo University, Shangluo 726000, China.

Computational Intelligence and Neuroscience
|July 7, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a BERT-based approach for cross-domain sentiment analysis, improving aspect-level sentiment classification. The method enhances feature representation similarity across domains for better performance.

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Cross-domain text sentiment analysis leverages source domain data to improve target domain annotation efficiency.
  • Effective cross-domain sentiment classification is crucial for resource optimization and enhanced utilization of annotated data.

Purpose of the Study:

  • To propose a BERT-based aspect-level sentiment analysis algorithm for cross-domain text.
  • To achieve fine-grained sentiment analysis across different domains with improved accuracy and efficiency.

Main Methods:

  • Utilizes BERT for extracting sentence-level and aspect-level representation vectors.
  • Employs an improved convolutional neural network for local feature extraction.
  • Integrates domain adversarial neural networks to minimize feature discrepancies between domains.
  • Forms sequence sentence pairs from aspect-level and sentence-level corpora.

Main Results:

  • The proposed algorithm demonstrates superior performance compared to classical algorithms on the Amazon product review dataset.
  • Achieved improved accuracy and F1 scores in cross-domain sentiment classification tasks.
  • Successfully enabled both sentence-level and aspect-level sentiment classification across domains.

Conclusions:

  • The BERT-based aspect-level sentiment analysis algorithm effectively addresses cross-domain challenges.
  • The domain adversarial approach enhances feature similarity, leading to robust cross-domain sentiment classification.
  • The model shows significant potential for real-world applications requiring fine-grained sentiment analysis across diverse text sources.