scholarly journals Mapping the peer-reviewed literature on accommodating nurses’ return to work after leaves of absence for mental health issues: a scoping review

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Christine L. Covell ◽  
Shamel Rolle Sands ◽  
Kenchera Ingraham ◽  
Melanie Lavoie-Tremblay ◽  
Sheri L. Price ◽  
...  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nada Alattar ◽  
Anne Felton ◽  
Theodore Stickley

Purpose Stigma associated with mental health problems is widespread in the Kingdom of Saudi Arabia (KSA). Consequently, this may prevent many Saudi people from accessing the mental health-care services and support they need. The purpose of this study is to consider how stigma affects people needing to access mental health services in the KSA. To achieve this aim, this study reviews the knowledge base concerning stigma and mental health in KSA and considers specific further research necessary to increase the knowledge and understanding in this important area. Design/methodology/approach This review examines the relevant literature concerning mental health stigma and related issues in KSA using the Arksey and O'Malley and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses frameworks. As a scoping review, it has used a systematic approach in literature searching. The results of the search were then thematically analysed and the themes were then discussed in light of the concepts of stigma and mental health. Findings Stigma around mental health impedes access to care, the nature of care and current clinical practice in the KSA. The voices of those with mental health issues in KSA are almost entirely unrepresented in the literature. Originality/value The review identifies that mental health stigma and cultural beliefs about mental health in KSA may act as barriers to accessing services. The voice of mental health service users in KSA remains largely unheard. If public discussion of mental health issues can increase, people’s experiences of accessing services may be improved.


2021 ◽  
Author(s):  
Paras Bhatt ◽  
Jia Liu ◽  
Yanmin Gong ◽  
Jing Wang ◽  
Yuanxiong Guo

BACKGROUND Artificial Intelligence (AI) has revolutionized healthcare delivery in recent years. There is an increase in research for advanced AI techniques, such as deep learning to build predictive models for the early detection of diseases. Such predictive models leverage mobile health (mHealth) data from wearable sensors and smartphones to discover novel ways for detecting and managing chronic diseases and mental health conditions. OBJECTIVE Currently, little is known about the use of AI-powered mHealth settings. Therefore, this scoping review aims to map current research on the emerging use of AI-powered mHealth (AIM) for managing diseases and promoting health. Our objective is to synthesize research in AIM models that have increasingly been used for healthcare delivery in the last two years. METHODS Using Arksey and O’Malley’s 5-point framework for conducting scoping reviews, we review AIM literature from the past two years in the fields of Biomedical Technology, AI, and Information Systems (IS). We searched three databases - informs PubsOnline, e-journal archive at MIS Quarterly, and ACM Digital Library using keywords such as mobile healthcare, wearable medical sensors, smartphones and AI. We include AIM articles and exclude technical articles focused only on AI models. Also, we use the PRISMA technique for identifying articles that represent a comprehensive view of current research in the AIM domain. RESULTS We screened 108 articles focusing on developing AIM models for ensuring better healthcare delivery, detecting diseases early, and diagnosing chronic health conditions, and 37 articles were eligible for inclusion. A majority of the articles were published last year (31/37). In the selected articles, AI models were used to detect serious mental health issues such as depression and suicidal tendencies and chronic health conditions such as sleep apnea and diabetes. The articles also discussed the application of AIM models for remote patient monitoring and disease management. The primary health concerns addressed relate to three categories: mental health, physical health, and health promotion & wellness. Of these, AIM applications were majorly used to research physical health, representing 46% of the total studies. Finally, a majority of studies use proprietary datasets (28/37) rather than public datasets. We found a lack of research in addressing chronic mental health issues and a lack of publicly available datasets for AIM research. CONCLUSIONS The application of AIM models for disease detection and management is a growing research domain. These models provide accurate predictions for enabling preventive care on a broader scale in the healthcare domain. Given the ever-increasing need for remote disease management during the pandemic, recent AI techniques such as Federated Learning (FL) and Explainable AI (XAI) can act as a catalyst to increase the adoption of AIM and enable secure data sharing across the healthcare industry.


2020 ◽  
Vol 12 (11) ◽  
pp. 89
Author(s):  
Jeavana Sritharan ◽  
Thivia Jegathesan ◽  
Dharshie Vimaleswaran ◽  
Ashvinie Sritharan

OBJECTIVES: The current COVID-19 pandemic continues to have a significant impact on the mental health of frontline workers worldwide. Currently there are limited published studies addressing mental health issues in frontline workers. The objective of this scoping review is to examine the range of existing global literature on mental health issues reported in frontline workers during the COVID-19 pandemic and to understand what mitigating factors exist. METHODS: The scoping review was guided by the Levac Colquhoun and O’Brien’s adapted version of Arkey and O’Malley’s framework. We performed a comprehensive search of three databases, Pubmed, APA PsychINFO, and CINAHL, identifying 684 studies. In total, 16 original studies and 4 letters to editors were included in this review. RESULTS: Of the original studies, 13 were published in China, and the remaining 3 in Italy, Turkey, and Iraq; all letters to editors were published in China. Sources of stress reported in frontline workers across studies included direct contact with COVID-19 patients, isolation, putting loved ones at risk, facing life and death decision making with COVID-19 patients, uncertainty with COVID-19 disease control, limited personal protective equipment, time spent thinking about COVID-19, limited staff/resources/pay, burnout, and stigma. Mental health symptoms and outcomes reported in frontline workers were fear, stress, anxiety, depression, insomnia, burnout, and psychological distress. CONCLUSION: Findings demonstrate the immediate need to increase mental health awareness and resources at an individual and system wide level. Mental health programs need to be catered towards each unique workplace to provide the necessary resources for frontline workers.


2020 ◽  
Vol 17 (12) ◽  
pp. 1275-1284
Author(s):  
Eduardo L. Caputo ◽  
Felipe F. Reichert

Background: This scoping review aimed to identify the available evidence related to physical activity (PA) and the coronavirus disease (COVID-19) pandemic. Methods: A search in 6 databases (PubMed, Embase, SPORTDiscus, Scopus, Web of Science, and CINAHL) was conducted on July 23, 2020. Medical subject headings and keywords related to PA and COVID-19 were combined to conduct the online search, which covered the period from January to July 2020. Results: Overall, 1784 articles were retrieved. After duplicate removal and title, abstract, and full-text screening, 41 articles were included. Most of the included studies were quantitative and collected data through online interviews/questionnaires, with sample sizes larger than 100 and composed by adults and older adults. Changes in PA levels due to the COVID-19 pandemic were the most assessed outcome, followed by the association between mental health issues and PA. Only 2 studies assessed the direct effects of PA on COVID-19. Conclusion: Most of the evidence identified a decrease in PA levels due to social distancing measures and that PA might help to decrease the mental health burden related to the COVID-19 outbreak.


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