New research gives a sharper conception of racial capitalism in California’s Inland Dmpire region

The growth of Amazon, Walmart, and other large firms dependent on sophisticated logistics has also led to the rise of US regions and local employment dominated by warehouses. In new research Paul Apostolidis explores racial capitalism and the warehouse industry’s impact among Latinx communities in California’s Inland Empire region. He writes that among Latinx workers and communities, racial capitalism not only involves insecurity, low pay, and workplace injury risks in warehouse employment, it also denies residents a safe, sanitary, and vibrant home life, undermining the essential basis for their lives to thrive and grow across generations

Introduction: On the Timeliness of Precarity-Critique Today

Now is a propitious time to re-examine the present forms and political implications of precarity. As my colleagues and I write the essays for this special issue of Emancipations, questions abound regarding the manifestations of precarity that have appeared since popular movements in France first adopted the slogan of ‘the precariat’ in the early 2000s. Then, ‘precarity’ fit as an intuitive name for people’s negatively altered living and working conditions as national governments, corporations and supra-national institutions consolidated neoliberal transformations as, indisputably, the new norm. Two decades hence, should this situation simply be reaffirmed as the not-so new normal

Meatpackers are deeply vulnerable to COVID-19. Expect a reckoning for US workers

As COVID-19 progresses, it will probably catalyse a long-overdue reckoning with the core structures of the political economy. This will be particularly important for the beleaguered meatpackers in the United States, writes Paul Apostolidis (LSE).

‘Neither work nor leisure’: motivations of microworkers in the United Kingdom on three digital platforms

This article examines the experience of microworkers living in the United Kingdom. Based on a survey of 1189 microworkers and 17 in-depth interviews, the article explores the experiences of UK-based microworkers on three digital platforms: Prolific, Clickworker and Amazon Mechanical Turk. The article draws on the theoretical framework of self-determination theory to analyse workers’ motivations for performing microwork. It reveals that workers’ relatively high satisfaction with otherwise low-paying and low-status work was possible because workers conceptualised their activity as occupying an ambiguous space and time in their lives, blurring traditional distinctions between work and leisure. These findings contribute to our understanding of how microworkers experience their relationship to work in the United Kingdom

The eSMART study protocol : a randomised controlled trial to evaluate electronic symptom management using the advanced symptom management system (ASyMS) remote technology for patients with cancer

Introduction While some evidence exists that real-time remote symptom monitoring devices can decrease morbidity and prevent unplanned admissions in oncology patients, overall, these studies have significant methodological weaknesses. The electronic Symptom Management using the Advanced Symptom Management System (ASyMS) Remote Technology (eSMART) study is designed to specifically address these weaknesses with an appropriately powered, repeated-measures, parallel-group stratified randomised controlled trial of oncology patients. Methods and analysis A total of 1108 patients scheduled to commence first-line chemotherapy (CTX) for breast, colorectal or haematological cancer will be recruited from multiple sites across five European countries.Patients will be randomised (1:1) to the ASyMS intervention (intervention group) or to standard care currently available at each site (control group). Patients in the control and intervention groups will complete a demographic and clinical questionnaire, as well as a set of valid and reliable electronic patient-reported outcome measures at enrolment, after each of their CTX cycles (up to a maximum of six cycles) and at 3, 6, 9 and 12 months after completion of their sixth cycle of CTX. Outcomes that will be assessed include symptom burden (primary outcome), quality of life, supportive care needs, anxiety, self-care self-efficacy, work limitations and cost effectiveness and, from a health professional perspective, changes in clinical practice (secondary outcomes). Ethics and dissemination Ethical approval will be obtained prior to the implementation of all major study amendments. Applications will be submitted to all of the ethics committees that granted initial approval.eSMART received approval from the relevant ethics committees at all of the clinical sites across the five participating countries. In collaboration with the European Cancer Patient Coalition (ECPC), the trial results will be disseminated through publications in scientific journals, presentations at international conferences, and postings on the eSMART website and other relevant clinician and consumer websites; establishment of an eSMART website (www.esmartproject.eu) with publicly accessible general information; creation of an eSMART Twitter Handle, and production of a toolkit for implementing/utilising the ASyMS technology in a variety of clinical practices and other transferable health care contexts. Trial registration number NCT02356081

Learning from Data to Predict Future Symptoms of Oncology Patients

Effective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms have the potential to assist oncology clinicians to personalize the patient’s treatment regimen more efficiently and provide more aggressive and timely interventions. Three common and inter-related symptoms in cancer patients are depression, anxiety, and sleep disturbance. In this paper, we elaborate on the efficiency of Support Vector Regression (SVR) and Non-linear Canonical Correlation Analysis by Neural Networks (n-CCA) to predict the severity of the aforementioned symptoms between two different time points during a cycle of chemotherapy (CTX). Our results demonstrate that these two methods produced equivalent results for all three symptoms. These types of predictive models can be used to identify high risk patients, educate patients about their symptom experience, and improve the timing of pre-emptive and personalized symptom management interventions. Document type: Articl

Congruence between latent class and k-modes analyses in the identification of oncology patients with distinct symptom experiences

CONTEXT: Risk profiling of oncology patients based on their symptom experience assists clinicians to provide more personalized symptom management interventions. Recent findings suggest that oncology patients with distinct symptom profiles can be identified using a variety of analytic methods. OBJECTIVES: The objective of this study was to evaluate the concordance between the number and types of subgroups of patients with distinct symptom profiles using latent class analysis and K-modes analysis. METHODS: Using data on the occurrence of 25 symptoms from the Memorial Symptom Assessment Scale, that 1329 patients completed prior to their next dose of chemotherapy (CTX), Cohen's kappa coefficient was used to evaluate for concordance between the two analytic methods. For both latent class analysis and K-modes, differences among the subgroups in demographic, clinical, and symptom characteristics, as well as quality of life outcomes were determined using parametric and nonparametric statistics. RESULTS: Using both analytic methods, four subgroups of patients with distinct symptom profiles were identified (i.e., all low, moderate physical and lower psychological, moderate physical and higher Psychological, and all high). The percent agreement between the two methods was 75.32%, which suggests a moderate level of agreement. In both analyses, patients in the all high group were significantly younger and had a higher comorbidity profile, worse Memorial Symptom Assessment Scale subscale scores, and poorer QOL outcomes. CONCLUSION: Both analytic methods can be used to identify subgroups of oncology patients with distinct symptom profiles. Additional research is needed to determine which analytic methods and which dimension of the symptom experience provide the most sensitive and specific risk profile

Learning from data to predict future symptoms of oncology patients

Effective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms have the potential to assist oncology clinicians to personalize the patient’s treatment regimen more efficiently and provide more aggressive and timely interventions. Three common and inter-related symptoms in cancer patients are depression, anxiety, and sleep disturbance. In this paper, we elaborate on the efficiency of Support Vector Regression (SVR) and Non-linear Canonical Correlation Analysis by Neural Networks (n-CCA) to predict the severity of the aforementioned symptoms between two different time points during a cycle of chemotherapy (CTX). Our results demonstrate that these two methods produced equivalent results for all three symptoms. These types of predictive models can be used to identify high risk patients, educate patients about their symptom experience, and improve the timing of pre-emptive and personalized symptom management interventions

Proceedings of Patient Reported Outcome Measure’s (PROMs) Conference Oxford 2017: Advances in Patient Reported Outcomes Research

A33-Effects of Out-of-Pocket (OOP) Payments and Financial Distress on Quality of Life (QoL) of People with Parkinson’s (PwP) and their Carer