BACKGROUND: Work-life balance practices are associated with employee perceptions of the need for achievement and well-being which subsequently influence their intention to leave the organization. This study contributes new knowledge to nursing studies on work-life balance in an Asian and Islamic society where the expectations for women are to focus on family rather than career.
DESIGN: A cross-sectional, explanatory mixed methodology.
METHODS: This is a two-phase study conducted between 2015-2017 with 401 nurses in East Malaysia. In Phase 1, researchers surveyed 379 nurses to test eight hypotheses and in Phase 2 researchers interviewed 22 nurses to explore the results of Phase 1.
RESULTS: Phase 1 revealed job satisfaction mediates the relationship between work-life balance practices (e.g. flexibility and choice in working hours, supportive supervision), financial success, and intention to leave. However, life satisfaction and money as a motivator did not mediate such relationships. Phase 2 identified four important factors that cast light on survey results: working conditions of Malaysian nurses; inadequate compensation in the public healthcare sector; team-based practices; and pressure on senior nurses in both administrative and clinical roles.
CONCLUSION: This is one of the first studies to investigate work-life balance issues among nurses in Malaysia. Outcomes of this study extend the debates on work-life balance and employee well-being in an Asian Islamic social context.
IMPACT: The use of flexible working arrangements and collectivist teamwork approaches, improving compensation and employment benefits and eliminating the 'time-based job promotion' policy may help to mitigate work-life balance issues and intention to leave among nurses in Malaysia.
OBJECTIVE: This review aims to synthesize the current evidence on digital health interventions targeting adolescents and young people with mental health conditions, aged between 10-24 years, with a focus on effectiveness, cost-effectiveness, and generalizability to low-resource settings (eg, low- and middle-income countries).
METHODS: We searched MEDLINE, PubMed, PsycINFO, and Cochrane databases between January 2010 and June 2020 for systematic reviews and meta-analyses on digital mental health interventions targeting adolescents and young people aged between 10-24 years. Two authors independently screened the studies, extracted data, and assessed the quality of the reviews.
RESULTS: In this systematic overview, we included 18 systematic reviews and meta-analyses. We found evidence on the effectiveness of computerized cognitive behavioral therapy on anxiety and depression, whereas the effectiveness of other digital mental health interventions remains inconclusive. Interventions with an in-person element with a professional, peer, or parent were associated with greater effectiveness, adherence, and lower dropout than fully automatized or self-administered interventions. Despite the proposed utility of digital interventions for increasing accessibility of treatment across settings, no study has reported sample-specific metrics of social context (eg, socioeconomic background) or focused on low-resource settings.
CONCLUSIONS: Although digital interventions for mental health can be effective for both supplementing and supplanting traditional mental health treatment, only a small proportion of existing digital platforms are evidence based. Furthermore, their cost-effectiveness and effectiveness, including in low- and middle-income countries, have been understudied. Widespread adoption and scale-up of digital mental health interventions, especially in settings with limited resources for health, will require more rigorous and consistent demonstrations of effectiveness and cost-effectiveness vis-à-vis the type of service provided, target population, and the current standard of care.
OBJECTIVES: In this paper, the Advanced Human-Robot Collaboration Model (AHRCM) approach is to enhance the risk assessment and to make the workplace involving security robots. The robots use perception cameras and generate scene diagrams for semantic depictions of their environment. Furthermore, Artificial Intelligence (AI) and Information and Communication Technology (ICT) have utilized to develop a highly protected security robot based risk management system in the workplace.
RESULTS: The experimental results show that the proposed AHRCM method achieves high performance in human-robot mutual adaption and reduce the risk.
CONCLUSION: Through an experiment in the field of human subjects, demonstrated that policies based on the proposed model improved the efficiency of the human-robot team significantly compared with policies assuming complete human-robot adaptation.