50716 research outputs found
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The role of attachment in palliative care transitions within Singapore nursing homes: perspectives from different stakeholders
No full textAntecedents and outcome of social media users’ health information sharing behaviour in developing countries: An empirical study on Bangladeshi social media users
No full textRedesigned design thinking: A study to redesign the design thinking process for better ideation outcomes of transdisciplinary teams innovating within wicked problem environments
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Salt: Delicious but Deadly - The ecological risk of saline effluents from coal seam gas and other hydrocarbon resources
No full textComparative analysis of existing and less explored compression techniques for ultrasound data transmission in telemedicine
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Turned from view: Navigating the productive potential of shame through the whirlpool approach
No full textInverse Dynamics Solution of an Upper Limb Rehabilitation Robot Using Deep Learning Approach
No full textTo efficiently control the motion of the upper limb rehabilitation robot, it is necessary that correct joint torques are provided. The analytical methods to compute the torque from the inverse dynamics are complex and intricate. For this purpose, a data driven approach based on deep learning model is presented in this study. The algorithm learns the position, velocities, and acceleration of the joints for a given trajectory and predicts the torque required. The results show the efficacy of the proposed algorithm in predicting the joint torques for the given dynamic parameters. The results obtained from this study can be further used in control of the upper limb rehabilitation robot.</p
Pilot implementation of an online program for family and friends supporting the mental health of paramedics in Australia:Lessons learned.
No full textIntroduction: Paramedics experience disproportionately high rates of mental health concerns, often relying on informal support from family and friends. While this support is vital, it can impose significant strain on family and friends, who frequently lack the necessary resources and skills for their role. To address this, a novel online program, Minds Together, was developed specifically for family and friends to enhance their ability to support the mental health of paramedics while prioritizing their own mental health and wellbeing. Methods: The feasibility and acceptability of the program was evaluated through a pilot study involving 53 participants randomized to either program or waitlist groups. Data collection included pre- and post-intervention surveys, program usage metrics, post-project surveys, and feedback from user testing. The Global Impact Analytics Framework (GIAF) guided analysis of planning, pre-engagement, pre-readiness, usability, dissemination, adoption, and contextual factors. Results: Participants valued the program for its lived-experience content and self-paced format. Broad dissemination reached over one million individuals, and usability and relevance were rated highly. However, low engagement and completion rates reflected challenges common to online interventions. Barriers included limited access duration, participants' time constraints, and difficulties in reaching the target audience. Suggestions included flexible access, advanced content options, and targeted outreach strategies. Conclusion: Minds Together shows promise as a scalable intervention for family and friends of paramedics. Future research will address barriers, explore long-term outcomes, and refine the program to better meet family and friends' diverse needs, improving mental health support for paramedics and their support network.</p