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As a UK-based group of female postgraduate researchers, the authors explored their experiences during COVID-19 pandemic through multivocal inquiry via a feminist collaborative autoethnographic project. In this paper, we use the Listening Guide as a tool to revisit and (re)analyse data from the aforementioned project, displaying findings in the form of voice poems. In utilising the Listening Guide, we discovered that listening is less of an exercise and more an art form. While the structured approach of the LG helped to enhance our understandings of wider individual experiences of disability and womanhood, identities that all authors inhabit, we were surprised to find that despite our established mutual trust and superficially similar experiences, we were unable to find emotional resonance through data that wasn't our own voice. We also found that the traditional stepped process of the LG that incorporates four listens to the data left our interpretations feeling flat. Through reflexivity and the novel collaborative approach we undertook in this analysis, we identified and implemented an augmentation of the Listening Guide process. In this paper, we propose an additional fifth listen, focusing on emotion, to facilitate a more holistic analysis of voice data. We explore how the fifth listen assisted the (re)construction of individual and collective identities, helping us to reshape our understandings. Finally, we elucidate the positives and pitfalls we experienced in the Listening Guide as a data analysis tool, recommending to other researchers the adoption of an iterative, flexible and reflexive approach in using it during collaborative research. [ABSTRACT FROM AUTHOR]

As a UK-based group of female postgraduate researchers, the authors explored their experiences during COVID-19 pandemic through multivocal inquiry via a feminist collaborative autoethnographic project. In this paper, we use the Listening Guide as a tool to revisit and (re)analyse data from the aforementioned project, displaying findings in the form of voice poems. In utilising the Listening Guide, we discovered that listening is less of an exercise and more an art form. While the structured approach of the LG helped to enhance our understandings of wider individual experiences of disability and womanhood, identities that all authors inhabit, we were surprised to find that despite our established mutual trust and superficially similar experiences, we were unable to find emotional resonance through data that wasn't our own voice. We also found that the traditional stepped process of the LG that incorporates four listens to the data left our interpretations feeling flat. Through reflexivity and the novel collaborative approach we undertook in this analysis, we identified and implemented an augmentation of the Listening Guide process. In this paper, we propose an additional fifth listen, focusing on emotion, to facilitate a more holistic analysis of voice data. We explore how the fifth listen assisted the (re)construction of individual and collective identities, helping us to reshape our understandings. Finally, we elucidate the positives and pitfalls we experienced in the Listening Guide as a data analysis tool, recommending to other researchers the adoption of an iterative, flexible and reflexive approach in using it during collaborative research. [ABSTRACT FROM AUTHOR]

This paper offers a multi-dimensional analysis of the ways and extent to which the US president and UK prime minister have securitized the Covid-19 pandemic in their public speeches. This assessment rests on, and illustrates the merits of, both an overdue theoretical consolidation of Securitization Theory's (ST) conceptualization of securitizing language, and a new methodological blueprint for the study of 'securitizing semantic repertoire'. Comparing and contrasting the two leaders' respective securitizing semantic repertoires adopted in the early months of the coronavirus outbreak shows that securitizing language, while very limited, has been more intense in the UK, whose repertoire was structured by a biopolitical imperative to 'save lives' in contrast to the US repertoire centred on the 'war' metaphor. [ABSTRACT FROM AUTHOR]

COVID-19 lockdown and social distancing measures have restricted funerals and memorial events and have limited the face-to-face social networks that grieving people might normally be able to draw upon for emotional support. However, while there is considerable expert informed speculation about the impacts of grief and "COVID bereavement", detailed accounts of experiences of bereavement and bereavement support during the pandemic have the potential to enrich and provide nuance and subtlety to the evidence base. This paper draws on diary accounts of bereavement support volunteers in the UK, who have been providing support for the bereaved through these challenging times. These reveal layers of complexity to the experiences of loss, grief and bereavement during these extraordinary times. However, they also point to a number of additional themes that lend a more positive valence to the suspension of normal social expectations and memorial practices associated with the pandemic, which, we argue should be reflected upon for their potential to address the discontents of contemporary governance of end of life and bereavement. [ABSTRACT FROM AUTHOR]

In the recent COVID-19 pandemic, a wide range of epidemiological modelling approaches were used to predict the effective reproduction number, R(t), and other COVID-19-related measures such as the daily rate of exponential growth, r(t). These candidate models use different modelling approaches or differing assumptions about spatial or age-mixing, and some capture genuine uncertainty in scientific understanding of disease dynamics. Combining estimates using appropriate statistical methodology from multiple candidate models is important to better understand the variation of these outcome measures to help inform decision-making. In this paper, we combine estimates for specific UK nations/regions using random-effects meta-analyses techniques, utilising the restricted maximum-likelihood (REML) method to estimate the heterogeneity variance parameter, and two approaches to calculate the confidence interval for the combined estimate: the standard Wald-type and the Knapp and Hartung (KNHA) method. As estimates in this setting are derived using model predictions, each with varying degrees of uncertainty, equal-weighting is favoured over the standard inverse-variance weighting to avoid potential up-weighting of models providing estimates with lower levels of uncertainty that are not fully accounting for inherent uncertainties. Both equally-weighted models using REML alone and REML+KNHA approaches were found to provide similar variation for R(t) and r(t), with both approaches providing wider, and therefore more conservative, confidence intervals around the combined estimate compared to the standard inverse-variance weighting approach. Utilising these meta-analysis techniques has allowed for statistically robust combined estimates to be calculated for key COVID-19 outcome measures. This in turn allows timely and informed decision-making based on all available information. [ABSTRACT FROM AUTHOR]