Browsing by Author "Shortland, Faye"
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Item Open Access The EPSRC's responsible innovation framework: to what extent does it influence research practice?(2023-02-22) Rose, David Christian; Shortland, Faye; Smith, Rachel; Schillings, JulietteThe world is on the cusp of a technology revolution in which radical technologies (e.g. AI, robotics, drones) offer the potential to transform society. The UK Government has committed to driving this through their Industrial Strategy, much of it distributed through UK Research Councils, to achieve aims such as Transforming Food Production and Clean Growth. It has been argued that responsible innovation principles - anticipation, inclusion, reflexivity, and responsiveness - should be embedded within this technology revolution so that the benefits, opportunities, and risks are properly considered. To this end, funders such as EPSRC are committed to responsible innovation. Yet, there has been little empirical work which investigates whether responsible innovation training actually changes design practices. Through surveys and interviews of EPSRC-funded researchers and PhD students, this pilot project investigates the impact of their responsible innovation framework (AREA) on research in practice.Item Open Access Farming wellbeing through and beyond COVID-19: stressors, gender differences, and landscapes of support(Wiley, 2023-02-17) Rose, David Christian; Budge, Hannah; Carolan, Michael; Hall, Jilly; Hammersley, Conor; Knook, Jorie; Lobley, Matt; Nye, Caroline; O'Reilly, Alexis; Shortland, Faye; Wheelier, RebeccaAlthough there has been a recent surge in research on drivers of poor farmer wellbeing and mental health, there is still a limited understanding of the state of wellbeing in farming communities around the world and how it can be best supported. This special issue seeks to extend our knowledge of how a combination of different stressors can challenge the wellbeing of farmers, farming families and farm workers, as well as how negative impacts can be unevenly distributed between different individuals. We advance the state of the art in research on farmer wellbeing, illustrating how social, economic and environmental policy drivers combine to create multiple points of stress, which are experienced differently by different individuals (e.g., age, gender). We move beyond an exploration of stressors towards a consideration of how landscapes of support for farmer wellbeing, and packages of support interventions, can improve the social resilience of farming communities. To be effective, these landscapes of support need to be accessible, well-funded, joined-up, and adaptable to evolving crises. This special issue explores farmer wellbeing in the context of global agricultural transitions, which are demanding new ways of farming (e.g., digitalisation, net zero, economic restructuring), and in light of shock events, such as the COVID-19 pandemic, in four countries—Ireland, New Zealand, the UK and the US. In exploring the impacts of future shock events and agricultural transitions on wellbeing, the issue concludes with a call to move beyond broad compilations of stressors and interventions and towards nuanced investigations of why and how poor farmer wellbeing occurs and how it can be best supported in specific contexts. The research from these four countries has wide relevance across European countries (similarity in farming systems, noting some differences), but a key message from the issue is that stressors on farmer wellbeing can be highly context-dependent according to place-based social, environmental, economic and political issues.Item Open Access The impact of COVID-19 on farmers’ mental health: a case study of the UK(Taylor and Francis, 2022-11-03) Rose, David Christian; Shortland, Faye; Hall, Jilly; Hurley, Paul D.; Little, Ruth; nye, Caroline; Lobley, MattObjectives In this paper, we use a UK case study to explore how the COVID-19 pandemic affected the mental health (emotional, psychological, social wellbeing) of farmers. We outline the drivers of poor farming mental health, the manifold impacts of the pandemic at a time of policy and environmental change, and identify lessons that can be learned to develop resilience in farming communities against future shocks. Methods We undertook a survey answered by 207 farmers across the UK, focusing on drivers of poor mental health and the effect of the COVID-19 pandemic. We also conducted 22 in-depth interviews with individuals in England, Scotland and Wales who provide mental health support to farmers. These explored how and why the COVID-19 pandemic affected the mental health of farmers. These interviews were supplemented by 93 survey responses from a similar group of support providers (UK-wide). Results We found that the pandemic exacerbated underlying drivers of poor mental health and wellbeing in farming communities. 67% of farmers surveyed reported feeling more stressed, 63% felt more anxious, 38% felt more depressed, and 12% felt more suicidal. The primary drivers of poor mental health identified by farmers during the pandemic were decreased social contact, issues with the general public on private land, and moving online for social events. Support providers also highlighted relationship and financial issues, illness, and government inspections as drivers of poor mental health. Some farmers, conversely, outlined positive impacts of the pandemic. Conclusion The COVID-19 pandemic is just one of many potential stressors associated with poor farming mental health and its impacts are likely to be long lasting and delayed. Multiple stressors affecting farmers at the same time can create a tipping point. Therefore, there is a need for long-term support and ongoing evaluation of the drivers of poor mental health in farming families.Item Open Access The EPSRC's responsible innovation framework: to what extent does it influence research practice?. Survey data(Cranfield University, 2023-04-05 10:24) Rose, David; Shortland, Faye; Smith, Rachel; Schillings, JulietteMethod: We also undertook an online Qualtrics survey of UK EPSRC-funded researchers and PhD students between September and November 2022. The survey was circulated via social media, by emailing interviewees, and by contacting CDT/DTP leads or administrators. Questions are shown in Appendix B. We received 138 responses (103 PhD students, 35 researchers/academics). In total, 30 institutions were represented in the responses with the top five institutions accounting for 76 responses and no more than 20 responses from any one single institution. At least 43[1] disciplines were covered. The gender response split was 87 Male, 43 Female, 2 Non-binary/non-conforming, with 5 preferring not to say or selecting other (no further detail provided). Descriptive statistics are provided for close-ended questions and open-ended answers were thematically coded. Software used: Qualtrics Format: Excel spreadsheet. Q1 and 2 were administrative questions and are not included. Anonymisation: Some open-ended question responses are removed or edited due to the possibility of identification. Where edits have been made, they are marked with xxx. · Q11: part anonymisation due to one answer giving identifiable information. · Q12_2: part anonymisation due to one answer giving identifiable information. · Q14: on training, fully removed as many answers identified their university or CDT. · Q20: giving further information, fully removed as many answers contained identifiable information. Q21: asked to name university. One respondent asked for this to be removed as it was too easy to identify themselves from a combination of discipline and university. Hence, the answers to this were fully removed for all. [1] Taking the first discipline given, as described by respondents (136 gave responses): Aeronautical engineering (1), Aerosol science (1), AI (2), Atmospheric Science (1), Automotive Engineering (1), Behavioural Science (1), Biochemical engineering (5), Biology (2), Biomaterials Science (1), Biomedical Science (4), Chemical Biology (1), Chemical Engineering (1), Chemistry (12), Computational Materials Modelling (1), Computer Science (13), Computing (5), Cybersecurity (2), Data Science (1), Economics (1), Electronic Engineering (3), Engineering (10), Environmental Science (1), Geography (1), Geospatial engineering (1), HCI (2), Health technology (2), Immunology (2), International relations (1), Machine Learning (3), Materials Science (6), Mathematical sciences (1), Mathematics (11), Mechanical engineering (2), Medical imaging (1), Neuroscience (3), Pharmaceutical Sciences (2), Physics (8), Psychology (9), Risk Analysis (1), Robotics (5), Social sciences (1), Sociology (1), Statistics (3).