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相关概念视频

Reliability and Validity01:29

Reliability and Validity

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Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
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Infertility in Females01:28

Infertility in Females

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Female infertility is defined as the inability to conceive after a year of regular, unprotected intercourse and affects about 10–15% of couples worldwide. The primary cause of female infertility is ovulatory disorders, which hinder the release of eggs. These disorders can be classified as hypothalamic amenorrhea, polycystic ovarian syndrome (PCOS), premature ovarian failure, and hyperprolactinemic anovulation disorders.
Endometriosis, a condition characterized by abnormal growth of...
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Infertility in Males01:23

Infertility in Males

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Male infertility affects millions of couples worldwide, arising from various factors that impact different stages of the reproductive process. An endocrine imbalance resulting from conditions like hypogonadism, Klinefelter syndrome, or pituitary disorders can disrupt hormone levels and reduce sperm production. Testicular defects, such as tumors, cryptorchidism, atrophic testes, abnormal sperm morphology, and low sperm count or motility, may arise due to genetic factors, structural...
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Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

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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...
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Regression Toward the Mean01:52

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Accuracy and Errors in Hypothesis Testing01:13

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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
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相关实验视频

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Breakfast Habits among Schoolchildren in the City of Uruguaiana, Brazil
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在Instagram上#不孕教育:使用DISCERN仪器在巴西进行可靠性分析.

Márcia Mendonça Carneiro1,2, Renata Bossi2, Debora Alvarenga2

  • 1Department of Gynecology and Obstetrics of the School of Medicine of the Federal University of Minas Gerais, Belo Horizonte, MG, Brazil.

JBRA assisted reproduction
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PubMed
概括

巴西的Instagram生育内容,主要来自医疗保健专业人员,往往缺乏清晰的信息来源和平衡. 虽然目标很明确,但信息来源和不确定性的缺失细节影响了可靠性.

关键词:
不孕不育 不孕不育 不孕不育患者教育 患者教育社交媒体 社交媒体

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科学领域:

  • 生殖健康和生育教育教育生殖健康和生育教育
  • 在医疗保健中的社交媒体分析.

背景情况:

  • 像Instagram这样的社交媒体平台越来越多地用于传播健康信息.
  • 了解生育相关内容的质量和作者权对于公共卫生至关重要.
  • 巴西的Instagram用户寻找关于不孕不育,试管婴儿和相关主题的信息.

研究的目的:

  • 评估巴西Instagram上的生育教育内容的流行程度和作者.
  • 使用DISCERN仪器评估这些信息的可靠性.
  • 识别社交媒体上分享的生育信息质量的差距.

主要方法:

  • 使用与生育相关的标签进行公共Instagram帖子的横截面分析.
  • 来自2021年3月的帖子被分析为作者 (医疗保健专业人士与普通人).
  • 使用DISCERN工具来评估信息的可靠性和质量.

主要成果:

  • 大多数分析的帖子 (89.2%) 是由医疗保健专业人员 (医生,诊所,相关医疗保健工作者) 撰写的.
  • 文章主要集中在生育治疗,疾病,检查和诊断上.
  • 虽然目标和相关性得分很高,但信息来源的清晰度,出版日期,余额以及不确定性/支持的细节始终很低.

结论:

  • 巴西人Instagram上的大多数生育内容都是由医生撰写的,并明确说明其目标.
  • 在提供有关数据来源,平衡和不确定性领域的信息方面存在重大缺陷.
  • 需要提高社交媒体平台上共享的生育信息的全面性和可靠性.