Late-Life Mental Health Education for Workforce Development: Brain Versus Heart?

Rosen, Jules; Stiehl, Emily; Mittal, Vikas; Fox, Debra; Hennon, John G.; Jeste, Dilip V.; Reynolds, Charles F.

doi:10.1016/j.jagp.2013.01.031

Cited by 7 publications

(6 citation statements)

References 7 publications

(7 reference statements)

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“…See Figure 7 for the comparison result between the expected value of the sample and the actual output value of the model. The system of reference [13] The system of reference [16] This paper system It can be seen from all the data that the modeling of psychological well-being based on BPNN has good accuracy and adaptability, the error of the modeling results is small, the fitting is good, and the modeling results are obviously better than those of CNN. In this paper, through the effective combination of the two algorithms, the advantages of the two algorithms are complementary.…”

Section: Results Analysis and Discussionmentioning

confidence: 99%

“…In order to further optimize the model recognition effect, Downs et al proposed a psychological well-being problem recognition algorithm based on the DeepPsy model [ 15 ]. Combining with the current situation and characteristics of the student group, Rosen et al expanded the evaluation factors by analyzing the reasons that affect the students' psychological well-being; they collected the corresponding data and used the NN model to predict and analyze the students' psychological well-being status [ 16 ]. Levecque et al revealed the main countermeasures and approaches for college students' psychological well-being education from the aspects of higher education authorities, university management, teachers, campus cultural environment, and the relationship between psychological well-being education and ideological and political education [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

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[Retracted] Design of Psychological Well‐Being Education Environment Scheme Based on Deep Learning Theory

Guo

2022

Journal of Environmental and Public Health

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This paper discusses the structure of psychological well-being education programmes in higher education institutions based on an analysis of the connotation and characteristics of deep learning theory, as well as the background of today’s talent training requirements, the psychological traits of contemporary students, and the practical requirements of the teaching reform of psychological well-being education courses in higher education institutions. A model for evaluating the psychological well-being of college students based on BPNN is presented in this paper, which also addresses the current severe shortage of full-time psychological counsellors. Additionally, the traditional BPNN is optimised by GA, and the resulting NN can better achieve the desired effect, demonstrating the viability of BPNN. It enables the psychological well-being of college students to be self-diagnosed online and significantly lessens the workload of psychological counselling institutions in higher education. According to the experimental findings, the optimised algorithm’s accuracy can reach 92.47 percent, and it is considered to be reliable. This study not only offers a novel approach to nonlinear data processing, but also paves the way for variable screening in the presence of an ambiguous structure. Additionally, in a limited sense, it offers insightful research for psychological education in higher education institutions.

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Section: Results Analysis and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

[Retracted] Design of Psychological Well‐Being Education Environment Scheme Based on Deep Learning Theory

Guo

2022

Journal of Environmental and Public Health

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“…The seclusion environment holds risk factors for tripping, slipping, and stumbling for patients who have mobility difficulties or have been sedated. Small environmental changes such as floor materials, lighting, and removal of doorsteps could help reduce risk factors for falls as extrinsic, environmental factors account for a significant proportion of falls 40. The findings of this study can be used to provide a basis for such interventions; the model can be used to steer the focus on the most significant risk factors.…”

Section: Discussionmentioning

confidence: 99%

Patient Falls in Seclusion Rooms in Psychiatric Inpatient Care

Varpula

Välimäki

Pulkkinen

2022

Journal of Nursing Care Quality

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Background: Patient falls are a major adverse event in psychiatric inpatient care. Purpose: To model the risk for patient falls in seclusion rooms in psychiatric inpatient care. Methods: Sociotechnical probabilistic risk assessment (ST-PRA) was used to model the risk for falls. Data sources were the research team, literature review, and exploration groups of psychiatric nurses. Data were analyzed with fault tree analysis. Results: The risk for a patient fall in a seclusion room was 1.8%. Critical paths included diagnosis of a psychiatric disorder, the mechanism of falls, failure to assess and prevent falls, and psychological or physical reason. The most significant individual risk factor for falls was diagnosis of schizophrenia. Conclusions: Falls that occur in seclusion events are associated with physical and psychological risk factors. Therefore, risk assessment methods and fall prevention interventions considering patient behavioral disturbance and physiological risk factors in seclusion are warranted.

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“…The mobility and instability of the elderly migrant population make their mental health easy to neglect [21]. Mental health education helps aging individuals understand basic knowledge about their psychological cognition and develop basic skills and methods to deal with psychological problems, prevent negative attitudes and reduce negative emotions such as anxiety and depression, which helps to enhance their physical and mental health [22][23][24][25]. Generally, the results of this study demonstrated that the average acceptance rate of mental health education among the elderly migrant population aged ≥ 60 years in China was 29.2%.…”

Section: Discussionmentioning

confidence: 99%

The current situation of mental health education and its influencing factors among the elderly migrant population in China: A cross-sectional study

Wang

Song

Fan

et al. 2022

Preprint

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Background Population mobility in China has increasingly included the middle-aged and elderly populations. Relatedly, the lack of mental health education among China's elderly migrant population is still an important but neglected problem. This study aims to understand the current situation of mental health education received by the elderly migrant population in China and to explore the related influencing factors. Methods This cross-sectional study included 5,729 elderly migrants who participated in the 2017 China Migrants Dynamic Survey. The covariates included four components: demographic and socioeconomic characteristics, mobility factors, public health service utilization, and social integration factors. Data were analyzed using the chi-square test for single factors and a binary logistic regression model for multiple factors. Results A total of 1672 elderly migrants received mental health education, for an acceptance rate of 29.2%. Educational methods included promotional materials (paper, film, and television), bulletin boards/electronic displays, public health consultations, health-related lectures, individualized face-to-face consultations and community SMS/WeChat/websites, which had been accessed by 90.1%, 80.2%, 70.0%, 67.5%, 48.8%, and 39.4% of the sample, respectively. Regression analysis showed that the older migrant population who had an average monthly household income > 3000 CNY, self-rated their health as healthy, had chronic diseases, had heard of National Basic Public Health Services, had established health records, were willing to integrate into the local population, and were highly involved in the community were more likely to receive mental health education. Older migrants who were ≥ 70 years old, had an elementary school education or below, had difficulties in the local community, migrated ≥ 11 years prior, moved for their offspring, and moved across provinces were less likely to receive mental health education. Conclusions The elderly migrant population does not accept mental health education well, and the means of obtaining educational assistance are relatively limited. Mental health interventions should be tailored to the characteristics of the migrant elderly population to increase mental health literacy and meet their psychological needs.

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Late-Life Mental Health Education for Workforce Development: Brain Versus Heart?

Cited by 7 publications

References 7 publications

[Retracted] Design of Psychological Well‐Being Education Environment Scheme Based on Deep Learning Theory

[Retracted] Design of Psychological Well‐Being Education Environment Scheme Based on Deep Learning Theory

Patient Falls in Seclusion Rooms in Psychiatric Inpatient Care

The current situation of mental health education and its influencing factors among the elderly migrant population in China: A cross-sectional study

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