Adverse childhood experiences exacerbate the association between day-to-day discrimination and mental health symptomatology in undergraduate students

Helminen, Emily C.; Scheer, Jillian R.; Edwards, Katie M.; Felver, Joshua C.

doi:10.1016/j.jad.2021.10.058

Cited by 14 publications

(8 citation statements)

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“…(2) The algorithm updates the cluster center m j according to (15). (3) The algorithm calculates the fuzzy covariance matrix F j and the positive definite matrix A j according to (10) and (11), respectively. (4) The algorithm calculates the similarity metric function D 2 jk according to ( 9).…”

Section: Advances In Multimediamentioning

confidence: 99%

“…While controlling the disease, it also produces great side effects, and people's brains and spirits are gradually numb. In fact, when mental health diseases are controlled in the early stage of drug treatment, the intervention of art, culture, psychology, and other services in the later rehabilitation is particularly important [11]. Native art therapy is a rehabilitation treatment for mental health disorders that is both natural and humanitarian.…”

Section: Introductionmentioning

confidence: 99%

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Construction of College Students’ Mental Health Assessment and Art Therapy System Aided by the Internet of Things and Big Data

2022

Advances in Multimedia

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In order to improve the effect of college students’ mental health assessment, this paper combines the Internet of Things and big data technology to build a college student’s mental health assessment system and analyzes and verifies the clustering effectiveness indicators by using FCM, GK, and GG clustering algorithms. Moreover, this paper explores the basic concepts and processes of FCM algorithm, GK algorithm, and GG algorithm and expounds the connection between the three algorithms. In addition, this paper uses four datasets to conduct clustering experiments and compares the CS indicator with several other indicators. The experimental results demonstrate the effectiveness of the CS indicator. The simulation study shows that the student mental health assessment system based on the Internet of Things and big data technology proposed in this paper can play a certain role in student mental health and art therapy, and it also verifies that art therapy plays a certain role in student psychotherapy.

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Section: Advances In Multimediamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Construction of College Students’ Mental Health Assessment and Art Therapy System Aided by the Internet of Things and Big Data

2022

Advances in Multimedia

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“…Artificial intelligence technology can enhance the pertinence and effectiveness of online public mental health education. At the same time, artificial intelligence technology has also brought severe challenges to the online public mental health education in colleges and universities [13][14][15]. e online public mental health education in colleges and universities must conform to the needs of the times and show its own value from more aspects.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] The Mining Method of Ideological and Political Elements in University Public Mental Health Courses Based on Artificial Intelligence Technology

2022

Journal of Environmental and Public Health

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Artificial intelligence technology has become an important part of the development of Internet technology. Artificial intelligence technology can help colleges and universities optimize the network ideological and political teaching system. Artificial intelligence technology provides more accurate data resources and rich and reliable educational technology means for online public mental health education in colleges and universities. This paper comprehensively uses a variety of methods such as qualitative and quantitative analysis, case and empirical analysis, literature analysis, and artificial intelligence technology. Artificial intelligence technology has been closely integrated with online public mental health education in colleges and universities. The model systematically analyzes the optimization methods of artificial intelligence technology methods for the online public mental health education system in colleges and universities, and constructs an innovation system for online public mental health education in colleges and universities. Based on the comprehensive analysis of artificial intelligence and public health in colleges and universities, this paper further proposes the application of artificial intelligence technology in online public mental health education in colleges and universities. On this basis, the model conducts an in-depth analysis of artificial intelligence technology and the online public mental health innovation system. The model supports the development of ideological and political teaching in colleges and universities through various forms such as idea innovation, path innovation, carrier innovation, and mechanism innovation.

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“…A history of ACEs may amplify how one is impacted by discrimination later in life. Previous research found that individuals who experienced four or more ACEs, experienced greater mental health symptoms in the presence of discrimination than those who experienced two or fewer ACEs [ 22 ]. To fully understand the impacts of discrimination, another potentially traumatic interpersonal stressor, on ED pathology, it should be studied within the context of ACEs.…”

Section: Introductionmentioning

confidence: 99%

The role of discrimination and adverse childhood experiences in disordered eating

et al. 2023

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Background In clinical research, there has been a call to move beyond individual psychosocial factors towards identifying cultural and social factors that inform mental health. Similar calls have been made in the eating disorders (ED) field underscoring the need to understand larger sociocultural influences on EDs. Discrimination is a social stressor that may influence mental health in similar ways to traumatic or adverse childhood experiences (ACEs). Given the high rates of EDs and discrimination among marginalized groups, it is vital to understand the role of discrimination and ACEs as predictors of ED symptoms in these populations. The aim of this study is to examine how perceived discrimination predicts ED pathology when statistically adjusting for gender, race, and ACEs. Methods The diverse study sample consisted of 331 undergraduate students from a longitudinal cohort study (ages 18–24; 66% female; 35% White/non-Hispanic). Participants completed measures of everyday discrimination, ACEs, and ED pathology. Results Following adjustment for multiple statistical comparisons, the frequency of daily discrimination predicted all ED symptoms above and beyond history of ACEs. In follow-up analyses, number of reasons for discrimination predicted cognitive restraint and purging. Differences in ED symptomatology were found based on the reason for discrimination, gender, and race. Specifically, those who experienced weight discrimination endorsed higher scores on all ED symptoms, and those experiencing gender discrimination endorsed higher body dissatisfaction, cognitive restraint, and restriction. People of color endorsed higher restriction, while female participants endorsed higher scores on all ED symptom with the exception of cognitive restraint. Conclusion Discrimination is a salient risk factor for ED symptoms even when accounting for individuals’ history of ACEs. Future research should utilize an intersectional approach to examine how perceived discrimination affects ED pathology over time. (Word count: 234).

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Adverse childhood experiences exacerbate the association between day-to-day discrimination and mental health symptomatology in undergraduate students

Cited by 14 publications

References 91 publications

Construction of College Students’ Mental Health Assessment and Art Therapy System Aided by the Internet of Things and Big Data

Construction of College Students’ Mental Health Assessment and Art Therapy System Aided by the Internet of Things and Big Data

[Retracted] The Mining Method of Ideological and Political Elements in University Public Mental Health Courses Based on Artificial Intelligence Technology

The role of discrimination and adverse childhood experiences in disordered eating

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