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相关概念视频

Autism Spectrum Disorder01:19

Autism Spectrum Disorder

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Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by persistent deficits in social communication and interaction alongside restrictive and repetitive behaviors or interests. ASD is sometimes accompanied by intellectual impairment.
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.
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Modeling in Therapy01:26

Modeling in Therapy

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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
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Case Studies01:22

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There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it.
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相关实验视频

Updated: Jul 18, 2025

Strategies for Assessing Autistic-Like Behaviors in Mice
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自闭症研究中的大数据:方法论上的挑战和解决方案

Brian K Lee1,2, Diana E Schendel1,2, Lindsay L Shea2

  • 1Department of Epidemiology and Biostatistics, Drexel University School of Public Health, Philadelphia, Pennsylvania, USA.

Autism research : official journal of the International Society for Autism Research
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PubMed
概括

大数据为自闭症研究提供了潜力,但需要仔细考虑方法. 严格的分析和跨学科的合作对于从自闭症研究的大数据集中提取有意义的见解至关重要.

关键词:
大数据就是大数据.流行病学流行病学

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科学领域:

  • 自闭症研究 自闭症研究
  • 科学中的大数据大数据
  • 神经发育障碍 神经发育障碍

背景情况:

  • 大数据在科学领域变得越来越突出.
  • 它在自闭症研究中的应用仍然未被充分探索.
  • 了解大数据的作用对于推进自闭症科学至关重要.

研究的目的:

  • 讨论大数据在自闭症研究中的相关性.
  • 突出自闭症大数据分析的方法挑战.
  • 强调在处理大型数据集时需要严格的方法.

主要方法:

  • 关于大数据原则的评论.
  • 讨论方法问题 (例如,混,数据错误).
  • 探索大数据对科学调查的影响.

主要成果:

  • 大数据为自闭症研究提供了独特的机会.
  • 方法论严谨对于减轻混和数据错误等风险至关重要.
  • 有效利用需要仔细的规划和执行.

结论:

  • 大数据并不是本质上优越的;质量和方法很重要.
  • "更大并不总是更好"适用于自闭症大数据研究.
  • 跨学科合作和健全的方法论是自闭症研究中大数据的有效见解的关键.