The effect of the virtual social network-based psycho-education on the hope of family caregivers of clients with severe mental disorders

Seyedfatemi, Naiemeh; Ahmadzad-Asl, Masoud; Bahrami, Raheleh; Haghani, Hamid

doi:10.1016/j.apnu.2021.02.004

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…With the continuous promotion of digital construction in colleges and universities, online education has rapidly entered the plan of college administrators [18]. Student behavior data analysis mainly refers to the use of data mining technology to mine the hidden information and patterns in student behavior log data, and then extract effective features to predict academic level.…”

Section: Analysis Of College Students' Behavior Datamentioning

confidence: 99%

Research on Detection Technology of Abnormal Data in College Physical Education Network Teaching Test Results

Shan,

2024

SCPE

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In recent years, with the rapid development of big data technology, more and more data are continuously generated with the summary of university systems. Therefore, how to use these educational data to provide more scientific decision-making information for university information builders is very important. This research collected various educational data through the network teaching system and campus information system. After that, the collected data was used to analyze the students' online behavior and to excavate valuable behavioral characteristics. In addition, the experiment also proposed indicators to describe students' behavior, such as network course behavior, network viscosity and life regularity, to provide the basis for the subsequent abnormal performance prediction model. Finally, the experiment used DBSCAN algorithm based on distance optimization for clustering analysis, and constructed a NA model based on multiple classifiers. The research results showed that when, the SC value was 0.711, which was the optimal solution of D-DBSCAN algorithm. At this time, the corresponding number of clusters was 4. When N=2, that was, the base classifier of NA model is composed of C4.5 model and SVM model, the prediction accuracy and time consumption are the most appropriate. The accuracy, recall and F1 values of NA model were 98.16\%, 97.26\% and 0.958 respectively, which was better than that of single model. To sum up, the NA model based on classifiers proposed in the study had higher accuracy and better model performance, can effectively reflect students' academic level, and could provide accurate abnormal performance data for college sports online teaching tests.

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Section: Analysis Of College Students' Behavior Datamentioning

confidence: 99%

Research on Detection Technology of Abnormal Data in College Physical Education Network Teaching Test Results

Shan,

2024

SCPE

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Relationship between Resilience and Caregiver Burden Among Home Caregivers of COVID-19 Patients

Manzari

Rafiei

Ghaderi

et al. 2023

Home Healthcare Now

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This study examined caregiver burden among home caregivers of COVID-19 patients and its relationship to resilience. This cross-sectional correlational study was conducted in Mashhad, Iran, in 2020. The sample consisted of 220 family caregivers of COVID-19 patients. The data collection tools included: demographic characteristics, Novak and Guest Caregiver Burden Inventory, and Connor-Davidson Resilience Scale. Data were analyzed with descriptive statistics and correlation test in SPSS v25. The mean score of caregiver burden was 76.85±16.25. In total, 4.5% experienced mild caregiver burden, 31.4% moderate caregiver burden, 50.9% severe caregiver burden, and 13.2% very severe caregiver burden. The mean score of resilience was 62.98±14.06. A significant and inverse relationship was observed between caregiver burden and resilience ( p < 0.05, r = -0.46). Family caregivers of COVID-19 patients experienced a significant level of caregiver burden, and it was lower in caregivers with higher levels of resilience. Further studies are recommended in this regard. The use of procedure and training that can improve the resilience of caregivers is recommended to nurses, especially home care nurses.

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Internalized stigma and its association with hope, self-esteem, self-efficacy, and treatment adherence among outpatients with severe mental illness: A cross-sectional survey

Hamidi

Ebrahimi

Vahidi

et al. 2023

Iranian J Nursing Midwifery Res

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Background: This study investigated the association between internalized stigma and hope, self-esteem, self-efficacy, and treatment adherence and explored the most influential and predictive factor of internalized stigma among patients with severe mental disorders. Materials and Methods: This correlational descriptive study was conducted on 257 outpatients diagnosed with severe mental illness according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) diagnostic criteria. The participants were seeking treatment at outpatient and affiliated clinics of Razi Hospital, Iran, from October 2018 to May 2019. We used a convenient sampling design. Internalized Stigma of Mental Illness scale, Dispositional Hope Scale, Rosenberg’s Self-Esteem Scale, General Self-Efficacy Scale, and Drug Attitude Inventory were used to gather data. The data were analyzed using inferential statistics (Pearson correlation, coefficient logistic, and regression analyses) at a 0.05 significance level. Results: The mean (standard deviation) score of internalized stigma was 1.57 (0.49), and 58% of the participants reported moderate to high internalized stigma. A negative significant relationship was found between internalized stigma and hope ( r = −0.55, p < 0.05), self-esteem ( r = −0.66, p < 0.05), and self-efficacy ( r = −0.64, p < 0.05). Treatment adherence was not found to be significantly associated with the internalized stigma. In the final regression model, self-esteem and self-efficacy significantly predicted internalized stigma. Conclusions: Given the crucial role of self-esteem and self-efficacy in predicting internalized stigma, nurses should devote special attention to these factors and use strategies to improve individuals’ self-esteem and self-efficacy.

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The effect of the virtual social network-based psycho-education on the hope of family caregivers of clients with severe mental disorders

Cited by 3 publications

References 26 publications

Research on Detection Technology of Abnormal Data in College Physical Education Network Teaching Test Results

Research on Detection Technology of Abnormal Data in College Physical Education Network Teaching Test Results

Relationship between Resilience and Caregiver Burden Among Home Caregivers of COVID-19 Patients

Internalized stigma and its association with hope, self-esteem, self-efficacy, and treatment adherence among outpatients with severe mental illness: A cross-sectional survey

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