117 Barriers and Facilitators Associated with the Implementation of Technical Improvements in Medical Beds 4 References 1. Ghersi, I., M. Mariño, and M.T. Miralles, Smart medical beds in patient-care environments of the twenty-first century: a state-of-art survey. BMC Medical Informatics and Decision Making, 2018. 18(1): p. 63. 2. Boniol, M., et al., The global health workforce stock and distribution in 2020 and 2030: a threat to equity and 'universal' health coverage? BMJ Glob Health, 2022. 7(6). 3. Ghersi, I., M. Mariño, and M. T Miralles, From Modern Push-Button Hospital-beds to 20th Century Mechatronic Beds: A Review. Journal of Physics: Conference Series, 2016. 705: p. 012054. 4. Tak, S.H., et al., Nurses' Perceptions About Smart Beds in Hospitals. CIN Computers Informatics Nursing, 2022. 41(6): p. 394-401. 5. Cresswell, K., et al., Theoretical and methodological considerations in evaluating large-scale health information technology change programmes. BMC Health Serv Res, 2020. 20(1): p. 477. 6. Bacchin, D., et al., Development and Testing of a Usability Checklist for the Evaluation of Control Interfaces of Electrical Medical Beds, in Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management AI, Product and Service. 2021. p. 3-19. 7. Tak, S.H., et al., Nurses' Perceptions About Smart Beds in Hospitals. Comput Inform Nurs, 2022. 8. Venkatesh, V., et al., User Acceptance of Information Technology Toward a Unified View. Management Information Systems Research Center, 2003. 27: p. 425-478. 9. Andhare, A. and A. Onkar, Human factors, ergonomic considerations and hospital bed designs: a review. International Journal of Human Factors and Ergonomics, 2022. 9(1): p. 47-72. 10. Moore, G.F., et al., Process evaluation of complex interventions: Medical Research Council guidance. Bmj, 2015. 350: p. h1258. 11. Moore, G.F., et al., Process evaluation of complex interventions UK medical Research Council (MRC) guidance 12. Rholdon, R., et al., Effects of Implementing a Simulation-Learning Based Training Using a TrainThe-Trainer Model on the Acquisition and Retention of Knowledge about Infant Safe Sleep Practices Among Licensed Nurses. J Pediatr Nurs, 2020. 55: p. 224-231. 13. Flottorp, S.A., et al., A checklist for identifying determinants of practice: a systematic review and synthesis of frameworks and taxonomies of factors that prevent or enable improvements in healthcare professional practice. Implement Sci, 2013. 8: p. 35. 14. Moser, A. and I. Korstjens, Series: Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis. Eur J Gen Pract, 2018. 24(1): p. 9-18. 15. Elo, S. and H. Kyngas, The qualitative content analysis process. J Adv Nurs, 2008. 62(1): p. 107-15. 16. Pearce, J., et al., The most effective way of delivering a train-the-trainers program: a systematic review. J Contin Educ Health Prof, 2012. 32(3): p. 215-226. 17. Santos, W.J., et al., The effectiveness of champions in implementing innovations in health care: a systematic review. Implement Sci Commun, 2022. 3(1): p. 80. 18. Nilsen, P. and S. Bernhardsson, Context matters in implementation science: a scoping review of determinant frameworks that describe contextual determinants for implementation outcomes. BMC Health Serv Res, 2019. 19(1): p. 189. 19. Groeneveld, S.W.M., et al., Underestimated Factors Regarding the Use of Technology in Daily Practice of Long-Term Care: Qualitative Study Among Health Care Professionals. JMIR Nursing, 2023. 6: p. e41032. 20. Alivar, A., et al., Smart bed based daytime behavior prediction in Children with autism spectrum disorder - A Pilot Study. Med Eng Phys, 2020. 83: p. 15-25. 21. Winger, T., et al., Fine tuned personalized machine learning models to detect insomnia risk based on data from a smart bed platform. Front Neurol, 2024. 15: p. 1303978.
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