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Background Following years of sustained pressure on the UK health service, there is recognition amongst health professionals and stakeholders that current models of healthcare are likely to be inadequate going forward. Therefore, a fundamental review of existing social models of healthcare is needed to ascertain current thinking in this area, and whether there is a need to change perspective on current thinking. Method Through a systematic research review, this paper seeks to address how previous literature has conceptualized a social model of healthcare and, how implementation of the models has been evaluated. Analysis and data were extracted from 222 publications and explored the country of origin, methodological approach, and the health and social care contexts which they were set. Results The publications predominantly drawn from the USA, UK, Australia, Canada and Europe identified five themes namely: the lack of a clear and unified definition of a social model of health and wellbeing; the need to understand context; the need for cultural change; improved integration and collaboration towards a holistic and person-centred approach; measuring and evaluating the performance of a social model of health. Conclusion The review identified a need for a clear definition of a social model of health and wellbeing. Furthermore, consideration is needed on how a model integrates with current models and whether it will act as a descriptive framework or, will be developed into an operational model. The review highlights the importance of engagement with users and partner organizations in the co-creation of a model of healthcare. [ABSTRACT FROM AUTHOR]

Background and Objectives Evidence-based practices to manage distress behaviors in dementia (DBD) are not consistently implemented despite demonstrated effectiveness. The Veterans Health Administration (VA) trained teams to implement Staff Training in Assisted Living Residences (STAR)-VA, an intervention to manage DBD in VA nursing home settings, or Community Living Centers (CLCs). This paper summarizes multiyear formative evaluation results including challenges, adaptations, and lessons learned to support sustained integration into usual care across CLCs nationwide. Research Design and Methods STAR was selected as an evidence-based practice for DBD, adapted for and piloted in VA (STAR-VA), and implemented through a train-the-trainer program from 2013 to 2018. Training and consultation were provided to 92 CLC teams. Evaluation before and after training and consultation included descriptive statistics of measures of clinical impact and survey feedback from site teams regarding self-confidence, engagement, resource quality, and content analysis of implementation facilitators and challenges. Results STAR-VA training and consultation increased staff confidence and resulted in significant decreases in DBD, depression, anxiety, and agitation for Veterans engaged in the intervention. Implementation outcomes demonstrated feasibility and identified facilitators and barriers. Key findings were interpreted using implementation frameworks and informed subsequent modifications to sustain implementation. Discussion and Implications STAR-VA successfully prepared teams to manage DBD and resulted in improved outcomes. Lessons learned include importance of behavioral health–nursing partnerships, continuous engagement, iterative feedback and adaptations, and sustainment planning. Evaluation of sustainment factors has informed selection of implementation strategies to address sustainment barriers. Lessons learned have implications for integrating team-based practices into system-level practice. [ABSTRACT FROM AUTHOR]

Background The application of foresight to the field of public health is limited. There is growing need to anticipate uncertain future trends and to plan for them. Foresight provides tools to experts and policymakers to discuss and plan for possible futures. Hence, the aim of this study is to illustrate how the foresight six-step approach can be applied in public health, and to provide recommendations on dealing with challenges, drawn from the Population Health Information Research Infrastructure (PHIRI) foresight exercise. Methods In this tutorial, we describe the six-step approach as part of foresight methodology and give examples of possible challenges. Step 1 comprises the formulation of study objectives. Step 2 focuses on developing a conceptual model and applying the Demographic Economic Sociocultural Technological Ecological and Political-Institutional (DESTEP) framework to identify and prioritize driving forces for the topic of interest. In Step 3, a time horizon and spatial level are defined. Step 4 discusses scenario logics. Steps 5 and 6 discuss different types of scenarios and associated tools for analyses. Possible challenges encountered whilst applying the foresight methodology at each of the steps, were drawn from experiences during PHIRI foresight exercise. Results Challenges associated with applying the foresight six-step approach included: formulating concise objectives, developing a conceptual model, understanding driving forces and uncertainty and difficulties in building scenarios. Conclusions Understanding concepts used in the six-step approach and how they relate to each other remained difficult. Support from foresight experts, conducting more foresight exercises, tutorials and guidelines can enhance understanding and support building capacity. [ABSTRACT FROM AUTHOR]

Background A measles epidemic affected the Nouvelle-Aquitaine region from November 2017 to May 2018 with clusters among Travellers. This indicates that measles vaccination rates among Travellers remain lower than in the general population. The objective of this study was to estimate the 'declarative vaccination' against measles, mumps and rubella (MMR) and to propose a conceptual framework to help identify determinants of MMR vaccination uptake among adult Travellers in Nouvelle-Aquitaine in 2019–20. Methods A cross-sectional study using random sampling was performed and included 612 adult Travellers from 1 November 2019 to 31 March 2020. A conceptual framework to model vaccination adherence was tested among this underserved population by using structural equation modelling. This model included five latent variables: health literacy, attitudes toward preventive measures, stigma, accessibility to care and perceived needs and five measured variables: information received on vaccination, perception of barriers, support for administrative documents, social support and housing conditions. Results Individuals who did not answer all the questions linked to the variables included in the model were excluded, thus 347 adults were included in the final sample. The declared vaccination rate against MMR was 74.0%, and 72.4% of the participants were favorable to vaccination. Vaccination adherence was significantly correlated with favorable attitudes toward preventive measures such as having a history of MMR vaccination and not having already refused a recommended vaccine and finally satisfactory information received on vaccination. Discussion To improve vaccination adherence, health authorities should lean on personal history with vaccination and on transmitting information on vaccination. [ABSTRACT FROM AUTHOR]

Background A pre-existing, well-established European influenza surveillance network known as I-MOVE enabled the rapid implementation of a European multi-country COVID-19 hospital surveillance network for surveillance of hospitalized COVID-19 cases in early 2020. This network included 257 hospitals in 11 surveillance sites across nine countries. We aimed to identify whether the surveillance objectives were relevant to public health actions, whether the surveillance system met its objectives, where and how shortcomings could be improved, and whether the system was sustainable. Methods We identified six key attributes (meeting objectives, usefulness, timeliness, data quality, simplicity and sustainability) to assess, using Centers for Disease Control and Prevention's evaluation framework. We analyzed pooled datasets, held interviews and group discussions with 10 participating and coordinating sites and gathered feedback through web surveys. Results There was overall agreement that the surveillance objectives had been met and being involved in a network of European partners had additional important benefits for stakeholders. While the publication of the outputs was not always sufficiently timely, data submission processes were considered straightforward and the key surveillance variables (age, sex, hospital admission and mortality data) were complete. The main challenges were identified as the collection of the large number of variables, limited available human resources and information governance and data protection laws. Conclusions I-MOVE-COVID-19 delivered relevant and accurate data supporting the development and implementation of COVID-19 surveillance. Recommendations presented here identify learning opportunities to support preparedness and surveillance response for future pandemics. The applied evaluation framework in this study can be adapted for other European surveillance system evaluations. [ABSTRACT FROM AUTHOR]

Objective Preterm infants are at high risk of neuromotor disorders. Recent advances in digital technology and machine learning algorithms have enabled the tracking and recognition of anatomical key points of the human body. It remains unclear whether the proposed pose estimation model and the skeleton-based action recognition model for adult movement classification are applicable and accurate for infant motor assessment. Therefore, this study aimed to develop and validate an artificial intelligence (AI) model framework for movement recognition in full-term and preterm infants. Methods This observational study prospectively assessed 30 full-term infants and 54 preterm infants using the Alberta Infant Motor Scale (58 movements) from 4 to 18 months of age with their movements recorded by 5 video cameras simultaneously in a standardized clinical setup. The movement videos were annotated for the start/end times and presence of movements by 3 pediatric physical therapists. The annotated videos were used for the development and testing of an AI algorithm that consisted of a 17-point human pose estimation model and a skeleton-based action recognition model. Results The infants contributed 153 sessions of Alberta Infant Motor Scale assessment that yielded 13,139 videos of movements for data processing. The intra and interrater reliabilities for movement annotation of videos by the therapists showed high agreements (88%–100%). Thirty-one of the 58 movements were selected for machine learning because of sufficient data samples and developmental significance. Using the annotated results as the standards, the AI algorithm showed satisfactory agreement in classifying the 31 movements (accuracy = 0.91, recall = 0.91, precision = 0.91, and F1 score = 0.91). Conclusion The AI algorithm was accurate in classifying 31 movements in full-term and preterm infants from 4 to 18 months of age in a standardized clinical setup. Impact The findings provide the basis for future refinement and validation of the algorithm on home videos to be a remote infant movement assessment. [ABSTRACT FROM AUTHOR]