Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Extraction: Advanced Methods00:56

Extraction: Advanced Methods

526
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
526
Data Collection by Experiments01:13

Data Collection by Experiments

25.2K
Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
25.2K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

2.9K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
2.9K
Data Collection by Survey01:07

Data Collection by Survey

7.0K
The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
7.0K
Data Collection by Observations01:08

Data Collection by Observations

12.8K
Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
12.8K
Data Collection II01:29

Data Collection II

8.5K
The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
8.5K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Reprioritising consultation in scoping reviews: Clarifying purposes and practices.

Medical education·2026
Same author

When the Math Does Not Add Up: Rethinking the Small-Grant Economy in Medical Education.

Academic medicine : journal of the Association of American Medical Colleges·2026
Same author

Artificial intelligence tools in scholarly publishing: guidance for peer reviewers.

Academic medicine : journal of the Association of American Medical Colleges·2026
Same author

The Presence and Nature of AI-Use Disclosure Statements in Medical Education Journals: A Bibliometric Study.

Perspectives on medical education·2026
Same author

"The Doctor Helped Me Decide": A Linguistic Analysis of Contraceptive Shared Decision-Making Narratives in Clinical Encounters Supported by a Decision Aid Mobile Application.

Perspectives on medical education·2026
Same author

Self-Directed Learning in Health Professions Education: A Systematic Review and Meta-Analysis.

Perspectives on medical education·2026
Same journal

Uncovering the economic costs of rotation in postgraduate clinical training: A UK case study using a novel methodology.

Medical teacher·2026
Same journal

The professional identity formation of undergraduate and postgraduate medical students: An umbrella review.

Medical teacher·2026
Same journal

'Place' and clinical reasoning development in context: An ethnographic case study.

Medical teacher·2026
Same journal

Fit for context: Faculty perspectives on mentorship and culture in Sub-saharan Africa.

Medical teacher·2026
Same journal

Evolving perceptions of the medical profession among medical students: A large-scale NLP analysis.

Medical teacher·2026
Same journal

The role of immersive extended reality for the development and evaluation of clinical reasoning in health professions education: A scoping review.

Medical teacher·2026
関連記事をすべて見る

関連する実験動画

Updated: Sep 9, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

570

知識合成のためのデータ抽出のための12のヒント

Lauren A Maggio1, Joseph A Costello1, Dario M Torre2

  • 1Department of Medical Education, University of Illinois Chicago, Chicago, IL, USA.

Medical teacher
|September 2, 2025
PubMed
まとめ
この要約は機械生成です。

この研究は,医学教育研究の重要なステップである知識合成におけるデータ抽出のための12の実践的なヒントを提供します. これらのガイドラインは,より良い教育実践と政策のための証拠の合成の厳しさと効率性を高めます.

キーワード:
データ抽出知識の統合文献レビュー

さらに関連する動画

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.0K

関連する実験動画

Last Updated: Sep 9, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

570
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.0K

科学分野:

  • 医学教育研究
  • 証拠合成の方法論

背景:

  • 医療教育の実践,研究,政策において,知識合成はますます影響力を発揮しています.
  • 知識合成に関する既存のガイドラインには,データ抽出に関する実用的な詳細が欠けていることが多い.
  • 欠陥や時間がかかるデータ抽出は,証拠合成の質を損なう可能性があります.

研究 の 目的:

  • 知識合成におけるデータ抽出に関する実践的指針の不足を補う.
  • データ抽出の質と効率を向上させるための実行可能な戦略を提供する.
  • 体系的なデータ抽出を行うための12のエビデンスベースのヒントを提供すること.

主な方法:

  • この記事は,文献と専門家の経験に基づいた,データ抽出のための12の実用的なヒントを提示しています.
  • チップは,データ抽出ツールを作成し,その使用を操作するように構成されています.
  • ガイドラインは,目的との抽出の調整,チームベースのアプローチ,不一致の解決,およびパイロットをカバーします.

主要な成果:

  • 12つのヒントは,体系的なデータ抽出のための具体的な戦略を提供します.
  • 提案された方法は,知識合成の厳密さ,効率性,信頼性を高めることを目的としています.
  • ツール開発とチーム環境での実装に関する実用的なアドバイスが提供されています.

結論:

  • 医療教育における高品質な知識合成には 効果的なデータ抽出が不可欠です
  • 提供されたヒントは,データ抽出プロセスを改善するための実用的な枠組みを提供します.
  • これらの戦略の実施は,教育の進歩のためのより信頼性と効率的なエビデンスの合成につながります.