ABSTRACT
Background: Internationally, COVID-19 has placed additional strain on already stretched health care systems. This has influenced how specialist palliative care organisations and their staff have been able to respond to clinical need in accordance with their professional values. Aims: To explore how COVID-19 impacted specialist palliative care staff and how organisations responded to the impact of the pandemic on staff well-being. Methods: Qualitative multiple case study, part of the CovPall study. Cases were English hospices providing specialist palliative care services in any setting. Semi structured interviews were carried out with hospice staff between 11/20-04/21, purposefully sampled by role, care setting and COVID-19 experience. Interviews were analysed using framework analysis. Results: Five cases (n=24 participants). Infection control constraints (e.g., visiting restrictions, PPE) prohibited and diluted participants' ability to provide care that reflected their core values. This resulted in staff experiencing moral distress. Despite organisational, team, and individual support strategies to address staff well-being, continually managing these constraints led to a 'crescendo effect' in which the impacts of moral distress (e.g., sadness, guilt, frustration, and fatigue) accumulated over time, sometimes leading to burnout. Some gained 'moral comfort' from solidarity with colleagues and making a valued contribution. Conclusions: This study provides a useful insight into why and how specialist palliative care staff experienced moral distress during COVID-19, and how organisations have responded. Despite their expertise in caring for the dying, staff well-being and mental health was affected by the pandemic. Prolonged experiences of moral distress has a detrimental impact on staff and the quality of care. Organisational, structural, and policy changes are urgently required to mitigate and manage these impacts.
ABSTRACT
Background: Palliative care and hospice services responded to the Covid-19 pandemic, but it is not clear how their workload has been affected. Aim: To identify factors associated with busyness in UK palliative care services during the Covid-19 pandemic. Methods: Online survey of clinical leads of palliative care services to understand the palliative care response to COVID-19 (CovPall). Study population characteristics were described. Unadjusted and multivariable ordinal logistic regression investigated factors (type of service, management type, number of confirmed (by test) Covid-19 cases, region, staff shortages) associated with busyness. Busyness was measured using a five-point scale (a lot less busy, slightly less busy, about the same, slightly more busy, a lot more busy). Results: 277 UK respondents: 33 Scotland, 4 Northern Ireland, 15 Wales, 225 in nine regions of England. Services reported being a lot more busy (71, 26%), slightly more busy (62, 22%), about the same (53, 19%), slightly less busy (50, 18%), much less busy (28, 10%). Multivariable analysis showed the following factors were associated with increased busyness: being a specialist palliative home care service (OR 1.93, 95% CI 1.15-3.25), providing hands on nursing care at home or in the community (OR 3.24, 95% CI 1.70-6.19), confirmed number (by test) Covid-19 cases (OR 1.01, 95% CI 1.00-1.01), being a publicly managed (NHS) service (OR 2.20, 95% CI 1.11-4.34), and experiencing staff shortages (OR 2.71, 95% CI 1.64-4.48). Conclusion: Community services and those publicly managed (NHS services) experienced increased busyness during Covid-19 pandemic. This suggests these services may have been more able to shift resources to respond to increased needs and changing patient priorities during the pandemic. This has implications for future funding models for palliative care.
ABSTRACT
Background: The disproportionately high death rate of those from ethnic minority groups from COVID-19 is well documented. Little is known about how palliative care services have responded. Aims: To map and develop insights into the response of palliative care services caring for patients and families from ethnic minority groups during the COVID-19 pandemic. Methods: (design, data collection, analysis): Cross-sectional on-line survey of UK palliative care services' response to COVID-19. Quantitative data were summarised descriptively and chi-square tests used to explore relationships between categorical variables. Open-ended responses were analysed using reflexive thematic analysis. Results: 93/277 (34%) responding palliative care services reported on care for COVID-19 patients from ethnic minority groups (60/123 (49%) hospital palliative care teams and 35/152 (23%) hospice inpatient teams). Nearly two-thirds (59%) of services offered care across multiple settings. Services who supported those from ethnic minority groups were more likely to have hospital palliative care teams (χ2 =15.21, p <0.001) and less likely to have inpatient hospice (χ2 = 30.11, p <0.001) or home palliative care teams (χ2 = 7.05, p= 0.008). 61/93 (66%) responding services stated there was no difference in how they supported or reached ethnic minority groups. Five main themes were identified from free-text comments: 1) disproportionate adverse impact of restricted visiting 2) compounded communication challenges 3) unmet religious and faith needs 4) mistrust of services and 5) equal but inequitable service response. Conclusion / Discussion: Policies introduced during the COVID-19 pandemic may have disproportionately adversely impacted those from ethnic minority groups. The palliative care response may have been both unequal and inequitable. The traditional focus of personalised care is not enough. We provide urgent recommendations for service providers and policymakers.
ABSTRACT
In Natural Language (NL) applications, there is often a mismatch between what the NL interface is capable of interpreting and what a lay user knows how to express. This work describes a novel natural language interface that reduces this mismatch by refining natural language input through successive, automatically generated semi-structured templates. In this paper we describe how our approach, called SKATE, uses a neural semantic parser to parse NL input and suggest semi-structured templates, which are recursively filled to produce fully structured interpretations. We also show how SKATE integrates with a neural rule-generation model to interactively suggest and acquire commonsense knowledge. We provide a preliminary coverage analysis of SKATE for the task of story understanding, and then describe a current business use-case of the tool in a specific domain: COVID-19 policy design.