Search

The data collection consists of 40 qualitative interviews with Polish migrant essential workers living in the UK and 10 in-depth expert interviews with key stakeholders providing information and support to migrant workers in the UK. All migrant interviews are in Polish. Six of the expert interviews with key stakeholders are in English and four are in Polish. Fieldwork was conducted fully online during the Covid-19 pandemic between March and August 2021, following the third UK-wide Covid-19 lockdown. Restrictions were still in place in some localities. Interviews took place shortly after the end of the transition period concluding the UK’s European Union exit on 1 January 2021. All Polish migrant worker interviewees entered the UK before 1 January 2021 and had the option to apply to the EU Settlement Scheme. The objectives of the qualitative fieldwork were to: 1. To synthesise empirical and theoretical knowledge on the short- and long-term impacts of COVID-19 on migrant essential workers. 2. To establish how the pandemic affected Polish migrant essential worker's lives; and expert interviews with stakeholders in the public and third/voluntary sector to investigate how to best support and retain migrant essential workers in COVID-19 recovery strategies. The project also involved: - co-producing policy outputs with partner organisations in England and Scotland; and - an online survey to measure how Polish migrant essential workers across different roles and sectors were impacted by COVID-19 in regard to health, social, economic and cultural aspects, and intentions to stay in the UK/return to Poland (deposited separately to University of Sheffield). Key findings included significant new knowledge about the health, social, economic and cultural impacts of Covid-19 on migrant essential workers. Polish essential workers were severely impacted by the pandemic with major mental health impacts. Mental health support was insufficient throughout the UK. Those seeking support typically turned to private (online) services from Poland as they felt they could not access them in the UK because of language or cultural barriers, lack of understanding of the healthcare system and pathways to mental health support, support being offered during working hours only, or fear of the negative impact of using mental health services on work opportunities. Some participants were in extreme financial hardship, especially those with pre-settled status or those who arrived in the UK during the pandemic. The reasons for financial strain varied but there were strong patterns linked to increased pressure at work, greater exposure to Covid-19 as well as redundancies, pay cuts and rejected benefit applications. There was a tendency to avoid applying for state financial support. These impacts were compounded by the sense of isolation, helplessness, or long-distance grief due to inability to visit loved ones in Poland. Covid-19 impacted most detrimentally on women with caring responsibilities, single parents and people in the health and teaching sectors. The most vulnerable Polish migrant essential workers - e.g. those on lower income, with pre-existing health conditions, restricted access to support and limited English proficiency - were at most risk. Discrimination was reported, including not feeling treated equally in the workplace. The sense of discrimination two-fold: as essential workers (low-paid, low-status, unsafe jobs) and as Eastern Europeans (frequent disciplining practices, treated as threat, assumed to be less qualified). In terms of future plans, some essential workers intended to leave the UK or were unsure about their future place of residence. Brexit was a major reason for uncertain settlement plans. Vaccine hesitancy was identified, based on doubts about vaccination, especially amongst younger respondents who perceived low risks of Covid-19 for their own health, including women of childbearing age, who may have worries over unknown vaccine side-effects for fertility. Interview participants largely turned to Polish language sources for vaccination information, especially social media, and family and friends in Poland. This promoted the spread of misinformation as Poland has a strong anti-vaccination movement.

Background: A small number of studies have used computer algorithms with areal-level data to detect community borders. One such study by Dean et al. uses a statistical model to detect sharp community boundaries, referred to as social frontiers. However, to date, none of these studies has validated the accuracy of their algorithms externally using human participants or other means. Therefore evidence base for the usefulness of these algorithms for policy and researchers has not been developed yet. Furthermore, data and algorithmic issues could lead to i) misleading inferential results using these borders and ii) misinformation once the results of these algorithms are made available to the public and policymakers. Existing literature: The existence of a boundary between two communities could lead to several positive or negative outcomes. Studies using algorithms to detect community boundaries have found that these boundaries are associated with more or less crime/ public disorder. Legewie and Schaeffer (@2016) use an edge detection algorithm from image processing whilst Dean et al. (@2019) chose borders based on a Bayesian statistical model. However, neither study validated their algorithm nor described how they picked most of the tuning parameters in their model. Need for a trial: The need for external validation of the results of algorithms is well accepted in fields like machine learning. Similarly, the validation of measurement instruments is commonplace in psychology. The lack of external validation leads to misleading results as well as a range of scientific issues ranging from measurement error to fundamental scientific issues such as a lack of falsifiability. This trial will establish an evidence base for the external validity of these detection algorithms starting with the sharp border detection algorithm in Dean et al. (@2019). The measure of sharpness is based on the proportion of foreign-born residents on either side of a border in 2011. Sharper borders are theorised to indicate the location of strong community borders or social frontiers. Objectives Primary objective: Determine whether residents and experts perceive sharper borders (as determined by the algorithm) as indicative of more clearly defined community borders. Hypothesis: The algorithm predicts which borders are more likely to be clearly defined community borders. The hypothesis is that participants will agree with the predictions of the algorithm. Secondary objective: Determine the feasibility of this type of trial for future research. Trial design A discrete choice experiment where participants are given a pair of maps and asked to pick which map represents more defined community borders. Each participant is given three pair sets to complete. All maps cover the same geographical area. The pair sequence and the order of maps within each pair are randomised. The exercise is conducted as part of a longer semi-structured qualitative interview. The exercise is given at either the beginning or end of the interview (depending on whether experts or residents).