Public opinion about the UK government during COVID-19 and implications for public health: A topic modeling analysis of open-ended survey response data

Consequences of the Pandemic on Mental Health of Healthcare Workers in the NHS

In recent years, the public health system of the United Kingdom, the National Healthcare System (NHS), has encountered difficulties that have been acknowledged in research studies and public policy discussions, such as resignations and staff shortages. During the COVID-19 pandemic, NHS healthcare workers were confronted with demanding circumstances, exacerbating the constraints of an already struggling system. With this, the authors of this paper aim to better understand the relationships between frustration at work, fear of infection, working hours, and the turnover intention of healthcare workers during the pandemic. This study employed a mixed-methods research approach, as a questionnaire survey was conducted along with an online self-administered interview questionnaire. Using mediation and moderated mediation analyses, it was found that the indirect effect of frustration at work through fear of infection on turnover intention was positively significant. Working hours moderated the mediation effect of fear of infection on the relationship between frustration at work and turnover intention. Surprisingly, the conditional indirect effect of frustration at work on turnover intention through fear of infection was the strongest among those with short working hours. This evidence was supplemented with qualitative results that enhance the understanding of why healthcare workers want to leave the system and the actions that can be taken on the organisational and policy fronts to address this issue.

Corruption and hierarchy: a replication of studies 1c and 6 of Fath & Kay 2018

Corruption represents a complex problem firmly embedded within our societal structures, governments, and organizations. The current study aimed to build a clearer consensus on the extent to which perceptions of organizational corruption are associated with organizational hierarchy. Two high-powered close replications of studies 1c and 6 by Fath and Kay provide further evidence for the claim that taller organizational structures are associated with greater perceived potential for corruption, and that these perceptions may compromise subsequent trust-related outcomes. Our results reinforce the importance of organizational design and aim to inspire future works to consider the ways in which researchers and organizations can minimize corruption. Preregistration, data and materials can be found on the OSF: https://osf.io/zb5j2.

Towards the outbreak tail, what is the public opinion about wastewater surveillance in the United States?

National opinions on a wide variety of public health topics can change over time and have highly contextual nuances. This study is a follow-up to prior inquiries into the knowledge of wastewater-based epidemiology, privacy concerns surrounding sample collection, and the use of data acquired, along with privacy awareness from an online survey conducted in the metropolitan United States during the winter of 2023. Mentions of wastewater-surveillance-related terms in the media remained common. Towards the outbreak tail in 2023, public support for surveillance of toxins (91%), diseases (91%), terrorist threats (87%), illicit drugs (70%), prescription medications (69%), and gun residue (60%) remained high. There was less support for surveillance of alcohol consumption (49%), mental illness (46%), healthy eating (37%), and lifestyle behaviors (35%). In terms of geographic scale, most respondents supported citywide surveillance (85%) with markedly lower levels of support for smaller (less anonymous) geographic scales covered by specific locations. Wastewater surveillance does not receive the public pushback that other COVID-19-related health system actors have witnessed. Instead, the public supports the expansion of wastewater surveillance as a standard to complement public health tools in other areas of health protection.

Research on the evolution of cross-platform online public opinion for public health emergencies considering stakeholders

OBJECTIVE

To explore the different processes of the themes and emotional evolution of various stakeholders in the network public opinion of sudden public health emergencies at different stages of the public opinion evolution lifecycle.

METHODS

This paper proposes a cross-platform analysis method for online public opinion during the public health emergencies based on stakeholders. Firstly, data from multiple platforms are collected and integrated. Secondly, stakeholders are categorized and the stages of public opinion evolution are determined based on stakeholder theory and lifecycle theory. Finally, the Latent Dirichlet Allocation (LDA)+Word2vec model and Convolutional Neural Network (CNN) model are used to analyze the themes and emotional evolution of stakeholders during different stages of public opinion evolution.

RESULTS

There are differences in the evolution patterns of different types of stakeholders. The evolution process of stakeholders' focus points exhibits a two-stage transition from concentration to divergence. The focus points of stakeholders are closely associated with their respective social domains. The emotions of the public undergo a three-stage process of positive-negative-positive change.

CONCLUSIONS

This study can provide a reference for the government to have a more comprehensive understanding of the development trend of public opinion and reduce the negative impact of public opinion.

Extraction of time-related expressions using text mining with application to Hebrew

In this research, we extract time-related expressions from a rabbinic text in a semi-automatic manner. These expressions usually appear next to rabbinic references (name / nickname / acronym / book-name). The first step toward our goal is to find all the expressions near references in the corpus. However, not all of the phrases around the references are time-related expressions. Therefore, these phrases are initially considered to be potential time-related expressions. To extract the time-related expressions, we formulate two new statistical functions, and we use screening and heuristic methods. We tested these statistical functions, grammatical screenings, and heuristic methods on a corpus containing responsa documents. In this corpus, many rabbinic citations are known and marked. The statistical functions and the screening methods filtered the potential time-related expressions and reduced 99.88% of the initial expressions (from 484,681 to 575).

Online on the frontline: A longitudinal social media analysis of UK healthcare workers' attitudes to COVID-19 vaccines using the 5C framework

This paper explores vaccine hesitancy among healthcare workers (HCWs) in the UK, where different COVID-19 vaccines were being rolled out through a national vaccination campaign from 2020 to 2022, consisting of a first and second dose programme. Through a mixed-method approach using qualitative discourse analysis and network analysis of Twitter data, we assessed HCW perceptions and views about the administration and delivery of COVID-19 vaccines in the United Kingdom (UK). We were also interested in exploring HCWs' personal experiences and attitudes towards taking COVID-19 vaccines themselves. We drew upon sociology, ethics, communication studies and used research methods concentrating on social media and media analysis. By employing the '5C framework' of 'confidence, complacency, constraints, calculation, and collective responsibility' we evaluated a longitudinal selection of tweets to capture relevant factors driving vaccination views and behaviours among HCWs. We found differing positions expressed about COVID-19 vaccines and policy during the first dose compared with the second, through a drop in confidence compounded by supply and access issues, as well the news of a vaccine mandate for HCWs by the UK government in 2021. HCWs asked calculation questions to the community or brought forward competing pieces of information about vaccine policy and guidelines. Constraint levels in access issues were noted, especially for those with work and caregiving responsibilities, and student nurses found they did not have equal vaccination access. HCWs also displayed collective responsibility on social platforms to both encourage vaccination and express concerns through the organisation of social action against vaccine mandates.

User feedback on the NHS test & Trace Service during COVID-19: The use of machine learning to analyse free-text data from 37,914 England adults

Objectives

The UK government's approach to the pandemic relies on a test, trace and isolate strategy, mainly implemented via the digital NHS Test & Trace Service. Feedback on user experience is central to the successful development of public-facing Services. As the situation dynamically changes and data accumulate, interpretation of feedback by humans becomes time-consuming and unreliable. The specific objectives were to 1) evaluate a human-in-the-loop machine learning technique based on structural topic modelling in terms of its Service ability in the analysis of vast volumes of free-text data, 2) generate actionable themes that can be used to increase user satisfaction of the Service.

Methods

We evaluated an unsupervised Topic Modelling approach, testing models with 5-40 topics and differing covariates. Two human coders conducted thematic analysis to interpret the topics. We identified a Structural Topic Model with 25 topics and metadata as covariates as the most appropriate for acquiring insights.

Results

Results from analysis of feedback by 37,914 users from May 2020 to March 2021 highlighted issues with the Service falling within three major themes: multiple contacts and incompatible contact method and incompatible contact method, confusion around isolation dates and tracing delays, complex and rigid system.

Conclusions

Structural Topic Modelling coupled with thematic analysis was found to be an effective technique to rapidly acquire user insights. Topic modelling can be a quick and cost-effective method to provide high quality, actionable insights from free-text feedback to optimize public health Services.

Capturing the experiences of UK healthcare workers during the COVID-19 pandemic: A structural topic modelling analysis of 7,412 free-text survey responses

BACKGROUND

Healthcare workers (HCWs) have provided vital services during the COVID-19 pandemic, but existing research consists of quantitative surveys (lacking in depth or context) or qualitative interviews (with limited generalisability). Structural Topic Modelling (STM) of large-scale free-text survey data offers a way of capturing the perspectives of a wide range of HCWs in their own words about their experiences of the pandemic.

METHODS

In an online survey distributed to all staff at 18 geographically dispersed NHS Trusts, we asked respondents, "Is there anything else you think we should know about your experiences of the COVID-19 pandemic?". We used STM on 7,412 responses to identify topics, and thematic analysis on the resultant topics and text excerpts.

RESULTS

We identified 33 topics, grouped into two domains, each containing four themes. Our findings emphasise: the deleterious effect of increased workloads, lack of PPE, inconsistent advice/guidance, and lack of autonomy; differing experiences of home working as negative/positive; and the benefits of supportive leadership and peers in ameliorating challenges. Themes varied by demographics and time: discussion of home working decreasing over time, while discussion of workplace challenges increased. Discussion of mental health was lowest between September-November 2020, between the first and second waves of COVID-19 in the UK.

DISCUSSION

Our findings represent the most salient experiences of HCWs through the pandemic. STM enabled statistical examination of how the qualitative themes raised differed according to participant characteristics. This relatively underutilised methodology in healthcare research can provide more nuanced, yet generalisable, evidence than that available via surveys or small interview studies, and should be used in future research.

Public View of Public Health Emergencies Based on Artificial Intelligence Data

In the current environment where the network and the real society are intertwined, the network public view of public emergencies has involved in reality and altered the ecology of communal public views in China. A new online court of influence has been created, and it affected the trend of events. As the main type of public emergencies, public health emergencies are directly related to people's health and life insurance. Therefore, the public often pays special attention. At present, correct media guidance plays an irreplaceable and important role in calming people's hearts and stabilizing social order. If news and public view are left unchecked, it is likely to cause panic among the people. However, in reality, public view research has always been a research object that is difficult to intelligentize and quantify. Based on such a realistic background, the article conducts a research on public view of public health emergencies based on artificial intelligence data analysis. This study designs an expert system for network public view and optimizes the algorithm for the key problem: SFC deployment. Finally, the system was put into real news and public opinion research on new coronavirus epidemic prevention, and experimental tests were carried out. The experimental results have shown that in the new coronavirus incident, the nuclear leakage incident, and the epidemic prevention policy, the data obtained by the public through the Internet are 50%, 68.06%, and 64.35%, respectively. For the system function in this study, both ICSO and IPSO are far better than the optimization results of CSO and PSO. For most of the test functions, IPSO is better than ICSO's optimization results, which better fulfills the needs of the research content. This study will make an in-depth analysis of the evolution process of online public opinion on public emergencies from the macro-, meso-, and micro-perspectives, in order to analyze the dissemination methods and internal evolution mechanism of various public emergencies of online public opinion, which provides countermeasures and suggestions for the government to guide and manage network public opinion.

Trajectories of Compliance With COVID-19 Related Guidelines: Longitudinal Analyses of 50,000 UK Adults

BACKGROUND

Governments have implemented a range of measures focused on changing citizens' behaviors to lower the transmission of COVID-19. While international data shows that compliance did decline from the start of the pandemic, average trends could mask considerable heterogeneity in compliance behaviors.

PURPOSE

To explore trajectories of compliance with COVID-19 guidelines.

METHODS

We used longitudinal data on self-reported compliance from 50,851 adults in the COVID-19 Social Study collected across two waves of the pandemic in the UK (April 01, 2020-February 22, 2021). We modeled typical compliance trajectories using latent class growth analysis (LCGA) and used multinomial logistic regression to examine whether individual personality and demographic characteristics were related to compliance trajectories.

RESULTS

We selected a four-class LCGA solution. Most individuals maintained high levels of compliance and reported similar levels of compliance across the first and second waves. Approximately 15% of participants had decreasing levels of compliance across the pandemic, reporting noticeably lower levels of compliance in the second wave. Individuals with declining compliance levels were younger on average, in better physical health, had lower empathy and conscientiousness and greater general willingness to take risks.

CONCLUSIONS

While a minority, not all individuals have maintained high compliance across the pandemic. Decreasing compliance is related to several psychological traits. The results suggest that targeting of behavior change messages later in the pandemic may be needed to increase compliance.