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In general, research demonstrates that deprivation, education, health, and well-being are determinants of volunteering, and that volunteering can play an important role in building stronger communities and provides many benefits for individual health and well-being. This study concentrates on the effects of physical and mental health and well-being as predictors when the aspect of socio-economic impact has been minimised. It utilises a unique data set from a UK Housing Association community with generally high levels of deprivation. Data were analysed using bivariate probit regression. In contrast to previous findings, physical health and mental health were not significantly related to volunteering. The key finding was that mental well-being was significantly related to informal volunteering. [ABSTRACT FROM AUTHOR]

Background: Personas, based on customer or population data, are widely used to inform design decisions in the commercial sector. The variety of methods available means that personas can be produced from projects of different types and scale. Objective: This study aims to experiment with the use of personas that bring together data from a survey, household air measurements and electricity usage sensors, and an interview within a research and innovation project, with the aim of supporting eHealth and eWell-being product, process, and service development through broadening the engagement with and understanding of the data about the local community. Methods: The project participants were social housing residents (adults only) living in central Cornwall, a rural unitary authority in the United Kingdom. A total of 329 households were recruited between September 2017 and November 2018, with 235 (71.4%) providing complete baseline survey data on demographics, socioeconomic position, household composition, home environment, technology ownership, pet ownership, smoking, social cohesion, volunteering, caring, mental well-being, physical and mental health–related quality of life, and activity. K-prototype cluster analysis was used to identify 8 clusters among the baseline survey responses. The sensor and interview data were subsequently analyzed by cluster and the insights from all 3 data sources were brought together to produce the personas, known as the Smartline Archetypes. Results: The Smartline Archetypes proved to be an engaging way of presenting data, accessible to a broader group of stakeholders than those who accessed the raw anonymized data, thereby providing a vehicle for greater research engagement, innovation, and impact. Conclusions: Through the adoption of a tool widely used in practice, research projects could generate greater policy and practical impact, while also becoming more transparent and open to the public. JMIR Public Health Surveill 2021;7(2):e25037 doi:10.2196/25037 [ABSTRACT FROM AUTHOR]

Background: Following the introduction of an algorithm aiming to maximise life-years gained from liver transplantation in the UK (the transplant benefit score [TBS]), donor livers were redirected from younger to older patients, mortality rate equalised across the age range and short-term waiting list mortality reduced. Understanding age-related prioritisation has been challenging, especially for younger patients and clinicians allocating non-TBS-directed livers. We aimed to assess age-related prioritisation within the TBS algorithm by modelling liver transplantation prioritisation based on data from a UK transplant unit and comparing these data with other regions., Methods: In this population-based modelling study, serum parameters and age at liver transplantation assessment of patients attending the Scottish Liver Transplant Unit, Edinburgh, UK, between December, 2002, and November, 2023, were combined with representative synthetic data to model TBS survival predictions, which were compared according to age group (25-49 years vs ≥60 years), chronic liver disease severity, and disease cause. Models for end-stage liver disease (UKELD [UK], MELD [Eurotransplant region], and MELD 3.0 [USA]) were used as validated comparators of liver disease severity., Findings: Of 2093 patients with chronic liver disease, 1808 (86%) had complete datasets and liver disease parameters consistent with eligibility for the liver transplant waiting list in the UK (UKELD ≥49). Disease severity as assessed by UKELD, MELD, and MELD 3.0 did not differ by age (median UKELD scores of 56 for patients aged ≥60 years vs 56 for patients aged 25-49 years; MELD scores of 16 vs 16; and MELD 3.0 scores of 18 vs 18). TBS increased with advancing age (R=0·45, p<0·0001). TBS predicted that transplantation in patients aged 60 years or older would provide a two-fold greater net benefit at 5 years than in patients aged 25-49 years (median TBS 1317 [IQR 1116-1436] in older patients vs 706 [411-1095] in younger patients; p<0·0001). Older patients were predicted to have shorter survival without transplantation than younger patients (263 days [IQR 144-473] in older patients vs 861 days [448-1164] in younger patients; p<0·0001) but similar survival after transplantation (1599 days [1563-1628] vs 1573 days [1525-1614]; p<0·0001). Older patients could reach a TBS for which a liver offer was likely below minimum criteria for transplantation (UKELD <49), whereas many younger patients were required to have high-urgent disease (UKELD >60). US and Eurotransplant programmes did not prioritise according to age., Interpretation: The UK liver allocation algorithm prioritises older patients for transplantation by predicting that advancing age increases the benefit from liver transplantation. Restricted follow-up and biases in waiting list data might limit the accuracy of these benefit predictions. Measures beyond overall waiting list mortality are required to fully capture the benefits of liver transplantation., Funding: None., Competing Interests: Declaration of interests BMS reports receiving honoraria from Sandoz and Chiesi. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Personas, based on customer or population data, are widely used to inform design decisions in the commercial sector. The variety of methods available means that personas can be produced from projects of different types and scale., Objective: This study aims to experiment with the use of personas that bring together data from a survey, household air measurements and electricity usage sensors, and an interview within a research and innovation project, with the aim of supporting eHealth and eWell-being product, process, and service development through broadening the engagement with and understanding of the data about the local community., Methods: The project participants were social housing residents (adults only) living in central Cornwall, a rural unitary authority in the United Kingdom. A total of 329 households were recruited between September 2017 and November 2018, with 235 (71.4%) providing complete baseline survey data on demographics, socioeconomic position, household composition, home environment, technology ownership, pet ownership, smoking, social cohesion, volunteering, caring, mental well-being, physical and mental health-related quality of life, and activity. K-prototype cluster analysis was used to identify 8 clusters among the baseline survey responses. The sensor and interview data were subsequently analyzed by cluster and the insights from all 3 data sources were brought together to produce the personas, known as the Smartline Archetypes., Results: The Smartline Archetypes proved to be an engaging way of presenting data, accessible to a broader group of stakeholders than those who accessed the raw anonymized data, thereby providing a vehicle for greater research engagement, innovation, and impact., Conclusions: Through the adoption of a tool widely used in practice, research projects could generate greater policy and practical impact, while also becoming more transparent and open to the public., (©Andrew James Williams, Tamaryn Menneer, Mansi Sidana, Tim Walker, Kath Maguire, Markus Mueller, Cheryl Paterson, Michael Leyshon, Catherine Leyshon, Emma Seymour, Zoë Howard, Emma Bland, Karyn Morrissey, Timothy J Taylor. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 16.02.2021.)

This paper describes a novel approach to explore how regulators, working with patients and practitioners, may contribute to supporting person-centred care and processes of shared decision making in implementing professional standards and reducing harms. Osteopathic patients report high levels of patient care. However, areas of consultations less likely to be rated as high included "fully understanding your concerns," "helping you to take control," and "making a plan of action with you," suggestive of a paternalistic approach to care and a barrier to the effective implementation of standards. This programme explored how to support patients and practitioners to make more explicit what is important to support consultations with better communication in accordance with standards. A series of workshops took place involving approximately 80 participants, which explored and identified practitioner and patient values; these were themed to develop a common framework and tested using case studies. Aspects of what enables or presents a barrier to a positive consultation were further explored with real patient narratives, and a range of resources were subsequently developed, which may support patients and practitioners to make explicit what is important to them in a consultation. A series of approaches and tools were then developed for piloting including patient curriculum vitae; patient goal planner; patient animation to support preparation for an appointment; infographic: a patient poster or leaflet; practitioner reflective tool; and an audio recording to increase awareness and understanding of values-based practice. In conclusion, a range of approaches may help to support patients and practitioners to make explicit what is important to them in a consultation. The next phase of our programme will use a range of methods including cluster sampling, pre-testing and post-testing with the Consultation and Relational Empathy (CARE) measure tool, and interviews and focus groups with users and practitioners to demonstrate impact., (© 2019 The Authors. Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd.)