ABSTRACT
Background/aims: Palliative care for persons experiencing homelessness is often provided late or not at all. A three-faceted consultation service was expected to increase knowledge of and collaboration between professionals, and improve palliative care for this population. This service included 1) consultations between shelter staff and professionals in palliative care, 2) multidisciplinary meetings of these professionals, and 3) training and education of professionals. We aimed to evaluate this service implemented in three Dutch regions looking at the process of implementation and added value of the service. Methods: A mixed-method evaluation study using structured questionnaires, semi-structured (group) interviews, and an implementation diary. RE-AIM framework and CFIR-constructs were used to identify barriers and facilitators of the implementation process. Results: 34 consultations, 22 multidisciplinary meetings and 9 trainings were registered during the implementation period (September 2019 to June 2021). Mainly shelter staff were reached by the service. A need for improvement and an existing network facilitated adoption of the service, while limited skills of professionals hindered it. Implementation was facilitated among others by expertise of the consultant, and hindered by COVID-19 circumstances, staff shortages and lack of knowledge of palliative care. Embedding the service in regular, funded, meetings facilitated maintenance, while a small number of involved persons in this small-scale service hindered it. Added value was found mainly on supporting and connecting professionals, increasing their knowledge and practices, and improving quality of palliative care. Conclusions: Implementing a three-faceted consultation service requires time, effort, and a perceived need among professionals. Once established, the intervention can help professionals in connecting professionals of two disciplines, to identify palliative care needs, and to provide timely and better quality palliative care to homeless persons.
ABSTRACT
The current paper aims to analytically visualize the future outcomes that the post-pandemic India might have in store for its citizens. We use time series forecasting on various collected data and combined the statistics of economics-deciding parameters to forecast the trends that might be prevalent in the next year. Since, the data contains a single anomalous trend, even the Prophet model could not learn this property from the data since this trend is not seasonal in nature. The current study proposes a novel architecture to deal with these rare unusual trends by combining two models - one learning normal usual patterns and the other getting trained on usual as well as rare anomalous patterns. It could help in dealing with sudden hike patterns like due to COVID-19 in the data, and lead to better forecasting on future timeframes. We combined the results of two distinct time-forecasting models trained on two sets of data of varying timeline lengths, using parameters obtained from Least Squares Approximation (LSA). The LSA helps us find an approximate vector approximation so as to obtain a model performing closely to the actual. © 2021