Purpose: To compare two different parenting practices (parental monitoring and negotiated unsupervised time) and perceived parental trust in the reporting of health risk behaviors among adolescents.
Methods: Data were derived from 692 adolescents in 9th and 10th grades (x = 15.7 years) enrolled in health education classes in six urban high schools. Students completed a self-administered paper-based survey that assessed adolescents' perceptions of the degree to which their parents monitor their whereabouts, are permitted to negotiate unsupervised time with their friends and trust them to make decisions. Using gender-specific multivariate logistic regression analyses, we examined the relative importance of parental monitoring, negotiated unsupervised time with peers, and parental trust in predicting reported sexual activity, sex-related protective actions (e.g., condom use, carrying protection) and substance use (alcohol, tobacco, and marijuana).
Results: For males and females, increased negotiated unsupervised time was strongly associated with increased risk behavior (e.g., sexual activity, alcohol and marijuana use) but also sex-related protective actions. In males, high parental monitoring was associated with less alcohol use and consistent condom use. Parental monitoring had no affect on female behavior. Perceived parental trust served as a protective factor against sexual activity, tobacco, and marijuana use in females, and alcohol use in males.
Conclusions: Although monitoring is an important practice for parents of older adolescents, managing their behavior through negotiation of unsupervised time may have mixed results leading to increased experimentation with sexuality and substances, but perhaps in a more responsible way. Trust established between an adolescent female and her parents continues to be a strong deterrent for risky behaviors but appears to have little effect on behaviors of adolescent males.
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http://dx.doi.org/10.1016/s1054-139x(03)00100-9 | DOI Listing |
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School of Geosciences, Yangtze University, Wuhan 430100, China.
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December 2024
Department of Engineering Management and Systems Engineering, George Washington University, Washington, DC 20052, USA.
Effective network intrusion detection using anomaly scores from unsupervised machine learning models depends on the performance of the models. Although unsupervised models do not require labels during the training and testing phases, the assessment of their performance metrics during the evaluation phase still requires comparing anomaly scores against labels. In real-world scenarios, the absence of labels in massive network datasets makes it infeasible to calculate performance metrics.
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Zhejiang HOUDAR Intelligent Technology Co., Ltd., Hangzhou 310023, China.
In industrial contexts, anomaly detection is crucial for ensuring quality control and maintaining operational efficiency in manufacturing processes. Leveraging high-level features extracted from ImageNet-trained networks and the robust capabilities of the Deep Support Vector Data Description (SVDD) model for anomaly detection, this paper proposes an improved Deep SVDD model, termed Feature-Patching SVDD (FPSVDD), designed for unsupervised anomaly detection in industrial applications. This model integrates a feature-patching technique with the Deep SVDD framework.
View Article and Find Full Text PDFSci Rep
January 2025
Division of Experimental Psychology and Neuropsychology, Department of Education and Psychology, Freie Universität Berlin, Habelschwerdter Allee 45, 14195, Berlin, Germany.
Neuropsychological assessment has to consider the subjective and objective functional deficits of help-seeking individuals in several cognitive domains. Due to time constraints in clinical practice, several web-based approaches have been developed. The current study examined whether functional deficits in the mnestic and attentive domain can be predicted based on an unsupervised self-administered online assessment neuropsychological online screening (NOS): This screening includes self-reports and psychometric memory tests (face-name association, visual short-term memory).
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