METHOD: This systematic review used the preferred reporting items of Systematic Reviews and Meta-Analyses (PRISMA). We conducted a systematic review of randomized controlled and quasi-experimental studies published from the establishment of the database to October 2022. Marital self-disclosure interventions were conducted with both cancer patients and their spouses. Studies published in a language other than English or Chinese, and studies below a quality grade of C were excluded. Data were extracted through a standardized data collection form, and two reviewers independently extracted and evaluated the data. The quality of the included studies was assessed using the Cochrane Handbook of Systematic Reviews of Interventions, and a third reviewer adjudicated in case of disagreement. The data were synthesized by vote counting based on direction of effect according to the Synthesis Without Meta-analysis (SWiM) reporting guideline.
RESULTS: Thirteen studies were included in the review. Based on quality evaluation, three studies were categorized as grade A (good), and ten studies were grade B (moderate). Seven studies reported moderate rates of participant refusal and attrition. The structure and topics of marital self-disclosure varied across different studies. The five studies had various prespecified disclosure topics, such as fear of cancer recurrence, benefit finding, and emotional distress. The overall results suggest that marital self-disclosure interventions can improve physical and psychological health, enhance marital relationships, and increase self-disclosure ability.
CONCLUSION: The limited number of studies, small sample sizes, diverse intervention strategies, and methodological heterogeneity weakened the evidence base for the effectiveness of marital self-disclosure interventions. Therefore, further high-quality randomized controlled trials (RCTs) are recommended to confirm the effectiveness of such interventions. These studies should also evaluate the interventions' long-term impact, analyze optional topics and methods, identify key features, and explore the development of the best intervention program.
METHOD: A content analysis was conducted on the anonymous posts retrieved from the WSIF platform between 8th March 2020 and 7th July 2022. Around 1457 posts were initially selected for analysis which was reduced to 1006 after removing duplicates and non-relevant posts, such as queries about the addresses of the doctors and other non-mental health-related issues. A thematic analysis of the data was conducted using an inductive approach.
RESULT: The 1006 posts generated four themes and nine sub-themes. All the women mentioned mental health symptoms (n = 1006; 100%). Most also mentioned reasons for seeking mental healthcare (n = 818; 81.31%), healthcare-seeking behavior (n = 667; 66.30%), and barriers to seeking mental healthcare (n = 552; 54.87%). The majority of women described symptoms of stress, depression, and anxiety-like symptoms, which were aggregated under common mental health conditions. Mental health symptoms were ascribed to various external influences, including marital relationship, intrafamilial abuse, and insecurities related to the COVID-19 pandemic. A large proportion of posts were related to women seeking information about mental healthcare services and service providers (psychologists or psychiatrists). The analysis found that most women did not obtain mental healthcare services despite their externalized mental health symptoms. The posts identified clear barriers to women accessing mental health services, including low mental health literacy, the stigma associated with mental healthcare-seeking behavior, and the poor availability of mental health care services.
CONCLUSION: The study revealed that raising mass awareness and designing culturally acceptable evidence-based interventions with multisectoral collaborations are crucial to ensuring better mental healthcare coverage for women in Bangladesh.
METHODS: We Searched China National Knowledge Infrastructure Database, Wan fang Database, CQVIP Journal Database、Web of Science Core Collection, Elsevier SD, Springer Online Journals, Medline, EBSCO-ERIC, SAGE Online Journals, PsycINFO, PsycArticles and ProQuest Dissertations and Theses。85 studies (90 independent effect size) were included from 2016 to 2023。The pooled correlation coefficient of the association between fear of missing out and mobile phone addiction was calculated by a random effects model using Comprehensive Meta-Analysis(Version 3.3).
RESULTS: The main effect analysis revealed a high positive correlation between fear of missing out and mobile phone addiction (r = 0.47, 95%CI [0.44, 0.50]). Furthermore, the measurements of mobile phone addiction moderated the strength of the association between fear of missing out and mobile phone addiction, with the highest correlation measured using MPATS and the lowest correlation measured using MPDQ. The age, gender, year of publication, cultural background, and the measurements of fear of missing out had no significant effect on the correlation between fear of missing out and mobile phone addiction.
CONCLUSION: The results indicated that fear of missing out was closely related to mobile phone addiction, which complied with the I-PACE model. Psychological services and mental health services should be developed to reduce the emergence of fear of missing out in the digital age and thus alleviate dependence on devices.
METHODS: Meaningful work, coping strategies, and mental health were evaluated in empirical research based on a sample of 462 SME employees working in Malaysia. Structured questionnaires were used to collect the data and analyze it through Structural Equation Modelling (SEM) using AMOS 21.0.
RESULTS: The findings of the study show the importance of meaningful work in influencing the mental health of SME employees, particularly during a crisis like the COVID-19 pandemic. This suggests that the more they value and see their work as meaningful, the more capable they are of dealing with limitations and mental health problems associated with crises. The study also discovered a partial mediating role for coping strategies between employees' mental health and meaningful work.
CONCLUSION: This study encourages employees to constantly feel connected and discover continued possibilities to work and learn even during crisis situations. In order to improve human resource efficiency in emerging markets, managers and owners of SMEs must implement the model developed by the researchers.
OBJECTIVE: Therefore, this research aims to create a flexible mental health care architecture that leverages data-driven methodologies and ensemble machine learning models. The objective is to proficiently structure, process, and present data for positive computing. The adaptive data-driven architecture facilitates customized interventions for diverse mental disorders, fostering positive computing. Consequently, improved mental health care outcomes and enhanced accessibility for individuals with varied mental health conditions are anticipated.
METHOD: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the researchers conducted a systematic literature review in databases indexed in Web of Science to identify the existing strengths and limitations of software architecture relevant to our adaptive design. The systematic review was registered in PROSPERO (CRD42023444661). Additionally, a mapping process was employed to derive essential paradigms serving as the foundation for the research architectural design. To validate the architecture based on its features, professional experts utilized a Likert scale.
RESULTS: Through the review, the authors identified six fundamental paradigms crucial for designing architecture. Leveraging these paradigms, the authors crafted an adaptive data-driven architecture, subsequently validated by professional experts. The validation resulted in a mean score exceeding four for each evaluated feature, confirming the architecture's effectiveness. To further assess the architecture's practical application, a prototype architecture for predicting pandemic anxiety was developed.