避开对比作为强迫症的诊断特征:对比避开问卷的接受器-操作器特征曲线分析
Valerie S Swisher1, Michelle G Newman1
1The Pennsylvania State University, State College, PA, USA.
Journal of affective disorders
|September 19, 2024
概括
对比避开模型 (CAM) 通过测量对比避开的问卷有效地识别强迫症 (OCD). 这些工具在检测可能的强迫症方面表现出极好的准确性,支持焦虑和强迫症之间的共享机制.
科学领域:
- 精神病学是一个精神病学.
- 临床心理学 临床心理学
背景情况:
- 重复的负面思维是一种与强迫症相关的跨诊断机制.
- 对比回避模型 (CAM) 表明,患有泛性焦虑障碍 (GAD) 的个人可以使用担忧来避免情绪转变.
- 这项研究调查了CAM在检测可能的强迫症中的实用性.
研究的目的:
- 检查避免对比的问卷在检测可能的强迫症的预测效用.
- 探索对比避开和特定的强迫症症状尺寸之间的关系.
主要方法:
- 采用了接收机操作员特征 (ROC) 曲线分析.
- 本科生 (N=2880) 完成了避免对比度 (CAQ-GE和CAQ-W) 和强迫症症状的测量.
- 为分析,形成了可能的强迫症和非强迫症组 (分别为n=431和n=433).
主要成果:
- 在可能的强迫症和非强迫症组之间发现了CAQ-GE和CAQ-W得分的显著差异.
- 曲线下面的面积值表明,在预测可能的强迫症问题上,这两个问卷表都具有很好的准确性 (CAQ-GE为0.87,CAQ-W为0.88).
- 特定的强迫症症状维度,如不可接受的想法和对伤害的责任,与避开对比度的措施更强烈相关.
结论:
- 避免对比似乎是强迫症障碍的一个相关机制.
- 强迫症和焦虑症之间的共享机制需要进一步研究.
- 未来的研究应该考虑横截面设计和本科生样本限制.
相关概念视频
Receiver Operating Characteristic Plot
95
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
95
Obsessive-Compulsive Disorder
64
Obsessive-compulsive disorder (OCD) is a mental health condition characterized by recurrent obsessions, compulsions, or both, which consume significant time and interfere with daily functioning. Obsessions involve persistent, intrusive, and unwanted thoughts, images, or urges that evoke anxiety. Common examples include irrational fears of contamination or harm. Compulsions are repetitive behaviors or mental acts performed to reduce the anxiety caused by obsessions. For instance, individuals...
64
Sensitivity, Specificity, and Predicted Value
213
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Sensitivity is the...
213
Strategies for Assessing and Addressing Confounding
83
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
83
Goodness-of-Fit Test
3.3K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
3.3K
Multiple Comparison Tests
3.9K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
3.9K


