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関連する概念動画

Data Validation01:03

Data Validation

5.3K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
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Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

2.9K
Nursing diagnoses represent a problem validated by major defining characteristics. There are four categories of nursing diagnoses: problem-focused, risk, health promotion or wellness, and syndrome. The anatomy of a nursing diagnosis includes three components: problem statement or diagnostic label, defining characteristics, and related factors.
Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...
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Modeling in Therapy01:26

Modeling in Therapy

145
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.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
145
Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

2.8K
A nursing diagnosis is written when the nurse recognizes a cluster of essential patient data indicating health problems treated with independent nursing interventions. The standardized terminologies of a nursing diagnosis help nurses identify and treat patients' problems. Every electronic health record that uses nursing diagnosis must employ standard diagnostic terminology. Developing an efficient, individualized care plan begins with accurate nursing diagnoses.
There are thirteen domains...
2.8K
Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis01:24

Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis

1.7K
The nursing process provides a clinical decision-making framework for patients and families to establish and implement a personalized care plan. Since part of the nurse's duties is to teach patients, the steps of the nursing process are the most effective way to approach instruction. The nursing process and the teaching-learning process are inextricably linked.
It is critical to determine the patient's learning needs during the assessment. Determination of learning needs compounds data...
1.7K
Nursing Evaluation01:15

Nursing Evaluation

3.5K
The evaluation stage signals the end of the nursing process. The nurse gathers evaluative data to assess whether or not the patient has attained the expected results. Whereas the nurse collects data in the nursing assessment to identify the patient's health concerns, the evaluation stage data determines if the indicated health issues are resolved. Evaluative data collection includes two sections: the data acquired to evaluate patient outcomes and the time criteria for data collection.
3.5K

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Mechanical Ventilation Boot Camp Curriculum
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看護のシミュレーションに関する質問中心のプロンプトエンジニアリング:モデル開発と検証

Aeri Jang1, Moonju Oh2, Mi Ok Song1

  • 1Department of Nursing, Mokpo National University, Muan-gun, Jeollanam-do, Republic of Korea.

Nurse education in practice
|September 6, 2025
PubMed
まとめ
この要約は機械生成です。

この研究は,人工知能を駆使した看護シミュレーションの説明モデルを導入し,臨床的推論を強化します. 検証されたモデルは,看護学生のAI能力,知識,自信を大幅に改善しました.

キーワード:
チャットGPTデブリーフィング看護教育プロンプトエンジニアリング質問中心の学習シミュレーション

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科学分野:

  • 介護教育
  • 医療における人工知能
  • シミュレーションベースの学習

背景:

  • 現在の看護シミュレーション・ディブリーフィングモデルは プロンプトエンジニアリングのような AI 方法の統合が欠けている.
  • 介護教育のAIによる臨床的推論を強化するために 構造化された枠組みが必要である.

研究 の 目的:

  • 問い合わせを中心とした学習と看護シミュレーションのためのAIプロンプトエンジニアリングを統合したインストラクショナル・ディブリーフィングモデルを開発し,検証する.
  • モデルが看護学生のAI能力と臨床推論能力を改善する効果を評価する.

主な方法:

  • 4段階の開発アプローチ:文献レビュー,インストラクターインタビュー,専門家検証,外部評価.
  • インストラクターや教育技術専門家の参加による看護シミュレーション環境を利用した.

主要な成果:

  • 質問の種類をディブリーフィングフェーズとAIプロンプトエンジニアリング戦略に合わせたフレームワークを確立しました.
  • 強力な有効性 (CVI=3.67,IRA=1.0) とAI能力,クラス関心,知識,自信の有意な改善を達成しました.

結論:

  • 検証されたモデルは,AIを看護シミュレーションのディブリーフィングに統合するための構造化されたアプローチを提供します.
  • 医療従事者のためのAI強化の臨床問題解決スキルの開発において,より広範な応用の可能性を示した.