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相关概念视频

Hindsight Biases01:12

Hindsight Biases

3.4K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
3.4K
Contaminants and Errors01:16

Contaminants and Errors

88
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
88
Cause and Effect01:53

Cause and Effect

10.9K
While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
10.9K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

1.5K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.5K
Ethics in Research01:56

Ethics in Research

23.0K
Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, this has not always been the case. Modern researchers must demonstrate that the research they perform is ethically sound.
23.0K
Random and Systematic Errors01:20

Random and Systematic Errors

10.9K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
10.9K

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相关实验视频

Updated: Jun 23, 2025

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
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Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

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在学术课程中从数据伪造中学到的教训,使用根源分析:一个案例报告.

Racheli Silvern1, Rachel Shvartsur, Ilya Kagan

  • 1Author Affiliations: Department of Nursing, School of Health Sciences, Ashkelon Academic College, Ashkelon, Israel.

Nurse educator
|June 17, 2024
PubMed
概括

一个本科护理研讨会通过让学生使用根源原因分析工具分析事件来解决数据伪造问题. 这促进了关于道德行为的公开讨论,并改善了学习环境.

科学领域:

  • 护理教育 护理教育
  • 研究伦理学研究伦理学
  • 学术诚信 学术诚信

背景情况:

  • 伪造数据严重损害了研究的可信性,并产生了重大影响.
  • 保持研究诚信在学术和专业环境中至关重要.

研究的目的:

  • 在本科护理研究研讨会中详细说明数据伪造事件.
  • 概述在个人和班级层面实施的纠正行动.

主要方法:

  • 参与数据伪造的学生使用石川图和5M模型来识别因果因素.
  • 结果向班级展示,促进了小组学习体验.

主要成果:

  • 有指导的讨论揭示了学生对数据伪造原因的不同观点.
  • 学生们彻底检查了与事件相关的决策过程,动机和情绪.
  • 最初紧张的氛围演变成了开放和诚实的氛围.

结论:

  • 本案例研究将教育策略与组织文化发展相结合,使用风险管理和患者安全工具.
  • 学术界必须分析不良事件,让学生参与学习过程,并强调在护理和学术实践中整合道德准则.

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