HIV Stigma, Sexual Identity Stigma and Online Coping Strategy of Gay, Bisexual and Queer People Living with HIV: A Moderated Mediation Study.

INTRODUCTION: Living with HIV/AIDS is more difficult for gay, bisexual, and queer (G- BQ) people as they face stigma on both the disease and sexuality, which puts significant stress on coping with stressors, and online platforms have become an alternative coping channel. METHOD: This study investigated the use of online coping strategies in moderating the HIV stigma mediated by sexual identity stigma on mental health in Malaysia. 123 GBQ people living with HIV between the ages of 20 and 39 participated in the study, responding to the HIV Stigma - Short Form Scale, adapted China MSM Stigma Scale, Online Coping Inventory, and DASS-21. RESULT: Results were analyzed using OLS, and logistic regression path modeling showed a statisti- cally significant indirect effect of sexual identity stigma mediating HIV stigma on depressive (ab = 0.1362), anxiety (ab = 0.1259), and stress (ab = 0.1636) levels. Problem-focused online coping strategy was found to moderate the indirect association between HIV stigma and depression levels via sexual identity stigma at low (ß = 0.2110, SE = 0.0741, p<.05) and moderate levels (ß = 0.1168, SE = 0.0465, p<.05). The findings demonstrated the compounding link between HIV and sexual identity stigmas on mental health and how online coping strategies can be used as a helpful coping resource to manage depressive symptoms for this community and mental health practition- ers. CONCLUSION: These findings can be beneficial to generate a better understanding of how double stigmas play a role in mental health and the types of online coping strategies adopted to process the stressors for GBQ individuals living with HIV in Malaysia.

An adaptive data-driven architecture for mental health care applications.

Background: In the current era of rapid technological innovation, our lives are becoming more closely intertwined with digital systems. Consequently, every human action generates a valuable repository of digital data. In this context, data-driven architectures are pivotal for organizing, manipulating, and presenting data to facilitate positive computing through ensemble machine learning models. Moreover, the COVID-19 pandemic underscored a substantial need for a flexible mental health care architecture. This architecture, inclusive of machine learning predictive models, has the potential to benefit a larger population by identifying individuals at a heightened risk of developing various mental disorders. 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.

What is needed to build a personalized recommender system for K-12 students' E-Learning? Recommendations for future systems and a conceptual framework.

Online learning has significantly expanded along with the spread of the coronavirus disease (COVID-19). Personalization becomes an essential component of learning systems due to students' different learning styles and abilities. Recommending materials that meet the needs and are tailored to learners' styles and abilities is necessary to ensure a personalized learning system. The study conducted a systematic literature review (SLR) of papers on recommendation systems for e-learning in the K12 setting published between 2017 and 2021 and aims to identify the most important component of a personalized recommender system for school students' e-learning. Recommendations for later studies were proposed based on the identified components, namely a personalized conceptual framework for providing materials to school students. The proposed framework comprised four stages: student profiling, material collection, material filtering, and validation.

Escalating progression of mental health disorders during the COVID-19 pandemic: Evidence from a nationwide survey.

Since the first nationwide movement control order was implemented on 18 March 2020 in Malaysia to contain the coronavirus disease 2019 (COVID-19) outbreak, to what extent the uncertainty and continuous containment measures have imposed psychological burdens on the population is unknown. This study aimed to measure the level of mental health of the Malaysian public approximately 2 months after the pandemic's onset. Between 12 May and 5 September 2020, an anonymous online survey was conducted. The target group included all members of the Malaysian population aged 18 years and above. The Depression Anxiety Stress Scale (DASS-21) was used to assess mental health. There were increased depressive, anxiety and stress symptoms throughout the study period, with the depression rates showing the greatest increase. During the end of the data collection period (4 August-5 September 2020), there were high percentages of reported depressive (59.2%) and anxiety (55.1%) symptoms compared with stress (30.6%) symptoms. Perceived health status was the strongest significant predictor for depressive and anxiety symptoms. Individuals with a poorer health perception had higher odds of developing depression (odds ratio [OR] = 5.68; 95% confidence interval [CI] 3.81-8.47) and anxiety (OR = 3.50; 95%CI 2.37-5.17) compared with those with a higher health perception. By demographics, young people-particularly students, females and people with poor financial conditions-were more vulnerable to mental health symptoms. These findings provide an urgent call for increased attention to detect and provide intervention strategies to combat the increasing rate of mental health problems in the ongoing COVID-19 pandemic.