Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Response Surface Methodology01:16

Response Surface Methodology

604
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
604

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Evaluating student satisfaction and self-confidence across a scaffolded simulation curriculum in undergraduate nursing education.

Frontiers in medicine·2026
Same author

The cognitive construction of moral scenes: Associations of visuospatial ability and impulsivity with perspective and vividness in mental simulation.

Cortex; a journal devoted to the study of the nervous system and behavior·2026
Same author

Diplomatic monocultures in public health diplomacy: a narrative review on conference equity, participation and visibility.

Public health reviews·2026
Same author

Mechanisms of weight gain associated with valproic acid: a scoping review.

European archives of psychiatry and clinical neuroscience·2026
Same author

Standardised tube weaning in children with CHD: telemedical Netcoaching approach is as affective as on-site treatment.

Cardiology in the young·2026
Same author

[Niemanden zurücklassen in Zeiten des Klimawandels und der Digitalisierung? Barrieren für ein gesundes und klimafreundliches Leben bei pflegenden Angehörigen].

Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany))·2026

相关实验视频

Updated: Jan 16, 2026

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.3K

虚拟弹性,真实共识:基于VR的弹性干预方法框架,使用修改后的Delphi方法.

Martin Ernst1, Yvonne Prinzellner1, Nina Dalkner2

  • 1Centre for Digital Health and Social Innovation, University of Applied Sciences, St. Pölten.

Studies in health technology and informatics
|October 3, 2025
PubMed
概括
此摘要是机器生成的。

本研究详细介绍了一种修改后的Delphi方法,用于共同开发虚拟现实 (VR) 护士的弹性培训. 基于共识的方法集成了专家来优先考虑数字健康干预的内容和策略.

关键词:
数字健康干预数字健康干预修改后的德尔菲方法在护理中的弹性.利益相关者共同设计虚拟现实培训 虚拟现实培训

更多相关视频

Virtual Reality Experiments with Physiological Measures
07:09

Virtual Reality Experiments with Physiological Measures

Published on: August 29, 2018

13.2K
Simultaneous Application of Transcranial Direct Current Stimulation during Virtual Reality Exposure
08:20

Simultaneous Application of Transcranial Direct Current Stimulation during Virtual Reality Exposure

Published on: January 18, 2021

4.4K

相关实验视频

Last Updated: Jan 16, 2026

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.3K
Virtual Reality Experiments with Physiological Measures
07:09

Virtual Reality Experiments with Physiological Measures

Published on: August 29, 2018

13.2K
Simultaneous Application of Transcranial Direct Current Stimulation during Virtual Reality Exposure
08:20

Simultaneous Application of Transcranial Direct Current Stimulation during Virtual Reality Exposure

Published on: January 18, 2021

4.4K

科学领域:

  • 数字健康干预措施 数字健康干预措施
  • 医疗保健中的虚拟现实
  • 护理教育 护理教育

背景情况:

  • 开发数字健康干预措施需要强大的协同创作的方法.
  • 虚拟现实 (VR) 为创新的医疗保健培训提供了潜力.
  • XR2弹性项目专注于基于VR的护士弹性培训.

研究的目的:

  • 概述修改后的Delphi研究的方法设计和理由.
  • 支持数字健康干预措施的早期联合开发.
  • 引导类似的倡议参与,共识驱动的数字健康设计.

主要方法:

  • 一个经过修改的Delphi研究,涉及护理,心理学,教育和VR开发领域的专家.
  • 一个多轮的共识过程,以优先考虑内容,实施和上下文因素.
  • 为跨学科合作和利益相关者集成而调整德尔菲方法.

主要成果:

  • 优先考虑VR弹性培训的内容领域.
  • 确定有效的实施策略.
  • 考虑成功设计数字干预的上下文因素.

结论:

  • 修改后的Delphi方法是有效的共识驱动的数字健康干预措施的共同开发.
  • 跨学科的合作对于设计有效的基于VR的医疗保健培训至关重要.
  • 该方法提供了一个框架,用于将技术创新与参与式设计整合到医疗保健中.