71 research outputs found
The role of educative thought in the life and work of Antonio Gramsci
No full textMany philosophers have propounded a vision of an improved society, what distinguishes Antonio Gramsci is his continuous effort to make it happen by understanding the process in order to put into practice. Gramsci's conviction about the importance of educative development came from both theory and experience. While there has been considerable examination of Gramsci's work in relation to the Prison Notebooks, this study will seek to address a lacuna in Gramsci scholarship. Using Gramsci's philological method, I analyse Gramsci's pre-prison activity; his pre-prison articles and letters, which, together with his letters from prison, formed part of his educative mission. This educative process was necessary, in order to construct a new party which would develop a collective will, collaboratively, with the masses.In this study therefore, I explore the contexts and formative experiences of the first part of his life together with the intellectual sources from which Gramsci developed his later theories, making central hitherto underemphasised connections between them which informed his writing and ideas. I intend to illustrate that Gramsci's underlying purpose in his writing, and political activity, was not only practical, on how to create a new socialist ruling class, but also educative in forming the mindset and values of his comrades. So that in addition to outlining his vision of a new order, he implicitly guided or explicitly explained the processes by which the necessary changes in social relations and moral climate could be made in order to achieve it. Each person had to engage with the values of the new order so that each could contribute to the construction of a new robust state. It was essential to build a hegemony at the most profound level, one which was dependent on collective understandings and a collective will
'A fascinating time to be involved with research' Exploring the impact of COVID-19 on postgraduate psycho-oncology researchers
No full textKey pointsTo explore UK postgraduate researcher (PGRs) COVID-19 experiences, the British Psychosocial Oncology Society (BPOS) conducted an online survey23 respondents’ qualitative data were analysed thematically and summarised using the strengths, weaknesses, opportunities, and threats (SWOT) frameworkCOVID-19 offered opportunities to develop online skills, resilience, and adaptability, whilst opening wellbeing conversationsPGRs reported practical difficulties, social isolation, unhealthy work/life balance and concerns about future careersPGRs have had an unprecedented and continually evolving experience; BPOS and relevant institutions must continue to provide adequate support and development opportunities to safeguard their future
Oncology professionals' views on the use of antidepressants in cancer patients: a qualitative interview study
No full textObjectives: Emotional distress, including depression, is an important issue for cancer patients and their families. Guidelines recommend the use of antidepressant drugs (ADs) for the management of depression in cancer. This study explores the views of oncology professionals about the inclusion of ADs in treatment plans. Design: Semi-structured interview study. Data were analysed using framework analysis. Setting: A specialist cancer centre and six district general hospitals across the Yorkshire Cancer Network. Participants: 18 randomly selected professionals from lung, breast, urology and colorectal cancer teams: oncologists (n=8), surgeons (n=3), clinical nurse specialists (n=2) and ward nurses (n=5). Results: Three main themes emerged relating to professionals' attitudes, knowledge and behaviour. Positive attitudes were primarily expressed by nurses. However, negative views were expressed about the potential for over-reliance on ADs, and their use constituting ‘giving in’. Doctors reported a lack of confidence in the use of and knowledge about ADs with an associated reluctance to prescribe. The general practitioner (GP) was regarded as the most appropriate professional to prescribe ADs. Conclusions: Cancer professionals highlighted a need for training in the appropriate use of ADs. Further, this research suggests that negative attitudes towards antidepressants may be a factor in their exclusion from treatment plans. The GP is seen to have a key prescribing role for AD therapy; however, it is unclear whether the GPs is asked to do this. This research raises questions about the adequacy of ADs in cancer care and to what extent the GP is able to meet this need
Integrated care pathways for cancer survivors - a role for patient-reported outcome measures and health informatics
No full textModern cancer treatments have improved survival rates and changed the nature of cancer care. The acute and long-term physical and psychosocial comorbidities associated with treatment place increasing demands on healthcare services to provide suitable models of follow-up care for the survivor population. Aim. We discuss the value and challenges of incorporating patient-reported outcome measures (PROMs) and eHealth interventions into routine follow-up care. We draw on our 15 years’ experience of developing electronic systems for capturing patient-reported data in oncology settings, with particular reference to eRAPID a new online symptom reporting system for cancer patients. The redesign of healthcare pathways. New stratified care pathways have been proposed for cancer survivors with an emphasis on supported self-management and shared care. The potential role of PROMs in survivorship care pathways. PROMs can be used to evaluate rehabilitation services, provide epidemiological ‘Big Data’ and screen patients for physical and psychological morbidities to determine the need for further support. In addition, electronic PROMs systems linked to electronic patient records (EPRs) have the capability to provide tailored self-management advice to individual patients. Integration of PROMs into clinical practice. The successful clinical utilisation of PROMs is dependent on a number of components including; choosing appropriate questionnaires, developing evidence-based scoring algorithms, the creation of robust electronic platforms for recording and transferring data into EPRs, and training staff and patients to engage effectively with PROMs. Discussion. There is increasingly positive evidence for using PROMs and eHealth approaches to support cancer patients’ care during treatment. Much of what has been learnt can be applied to cancer survivorship. PROMs integrated into eHealth platforms and with EPR have the potential to play a valuable role in the development of appropriate and sustainable long-term follow-up models for cancer survivors
Molsidomine inhibits the chemoattractant-induced respiratory burst in human neutrophils via a no-independent mechanism
No full text3-Morpholino-sydnonimine (SIN-1) is a NO-releasing compound which mimics the effects of cGMP through activation of soluble guanylyl cyclase. Its prodrug, molsidomine (SIN-10), does not release NO but does modulate various cell functions. These findings prompted us to study the effects of SIN-10 and SIN-1 on the respiratory burst in human neutrophils. SIN-10 was more effective than SIN-1 in inhibiting superoxide anion (O2-) formation induced by N-formyl-L-methionyl-L-leucyl-L-phenylalanine (fMet-Leu-Phe) and by C5a. The effects of SIN-1 and SIN-10 on O2- formation were additive or less than additive, indicating the sydnonimines acted through a common mechanism. The sydnonimines showed no effect on O2- formations induced by gamma-hexachlorocyclohexane, arachidonic acid and a phorbol ester. They did not inhibit O2- formation induced by xanthine oxidase, by autoxidation of pyrogallol and in a cell-free system from HL-60 leukemic cells. Neutrophils did not convert SIN-10 to SIN-1 as assessed by O2 consumption which accompanies NO release from SIN-1. The cell-permeant analogue of cGMP, N2,2'-O-dibutyryl guanosine 3':5'-monophosphate (Bt2cGMP), and SIN-10 but not SIN-1 inhibited fMet-Leu-Phe-induced O2 consumption. SIN-1 and SIN-10 slightly enhanced agonist binding to formyl peptide receptors, whereas Bt2cGMP was inhibitory. The sydnonimines did not affect GTP hydrolysis of heterotrimeric regulatory guanine nucleotide-binding proteins in HL-60 membranes. SIN-1 but not SIN-10 stimulated ADP-ribosylation of a 39-kDa protein in the cytosol of HL-60 cells. SIN-10 reduced fMet-Leu-Phe-induced rises in cytosolic Ca2+ concentration in neutrophils. These data suggest that SIN-10 inhibits the respiratory burst via a NO-independent mechanism which may involve inhibition of rises in cytosolic Ca2+ concentration
Surfactant function in neonates with respiratory distress syndrome
No full textThe function of pulmonary surfactant of a group of 14 preterm neonates (birth weight 907 +/- 60 g) who suffered from severe respiratory distress syndrome (RDS) and who had received exogenous bovine lipid extracted surfactant on the first day of life was compared to that in a second group of 8 neonates (birth weight 940 +/- 110 g) with mild RDS who had not received surfactant treatment. Mechanical respiratory support from day 2 on was the same in both groups. The minimal surface tension (gamma(min)) improved steadily, falling from about 30 mN/m initially to less than 20 mN/m before extubation, A consistent but loose correlation was found between gamma(min) and mechanical respiratory support necessary, as quantitated by the oxygenation index. Total protein was about 0.8 +/- 0.2 mg/mg of phospholipids and did not change during the first week of life. There were no correlations between total protein and gamma(min) or the oxygenation index. The data suggest that inhibition of surfactant function by proteins leaked into the airspaces does not play a major role during recovery from RDS, Instead, endogenous remodelling of surfactant might be of greater relevance
“No turning back” Psycho‐oncology in the time of COVID‐19: Insights from a survey of UK professionals
No full textKey pointsTo gain insight on UK professionals' experiences and views of the impact of the COVID‐19 pandemic on psycho‐oncology activity, the British Psychosocial Oncology Society (BPOS) conducted an online survey of members and UK colleaguesQualitative data from 94 respondents were analysed thematically. Key themes were summarised using the strengths, weaknesses, opportunities and threats (SWOT) frameworkProfessionals reported severe disruptions in delivering clinical and supportive care to people affected by cancer and associated research activity. There were major concerns that the full impact of the pandemic is yet to be realised.In both care and research settings, the pandemic has also been an impetus for positive changes in working practices, technology adoption, reducing process barriers and fostering collaborations which has to potential to be sustained.To mitigate ongoing challenges, is it vital that cancer organisations work together to adapt and promote psycho‐oncology activity to maximise benefit for patients and professionals in the longer‐term
Using Machine Learning to Predict Unplanned Hospital Utilisation and Chemotherapy Management from Patient-Reported Outcome Measures
No full textPurpose
Adverse effects of chemotherapy often require hospital admissions or treatment management. Identifying factors contributing to unplanned hospital utilization may improve health care quality and patients' well-being. This study aimed to assess if patient-reported outcome measures (PROMs) improve performance of machine learning (ML) models predicting hospital admissions, triage events (contacting helpline or attending hospital), and changes to chemotherapy.
Materials and Methods
Clinical trial data were used and contained responses to three PROMs (European Organisation for Research and Treatment of Cancer Core Quality of Life Questionnaire [QLQ-C30], EuroQol Five-Dimensional Visual Analogue Scale [EQ-5D], and Functional Assessment of Cancer Therapy-General [FACT-G]) and clinical information on 508 participants undergoing chemotherapy. Six feature sets (with following variables: [1] all available; [2] clinical; [3] PROMs; [4] clinical and QLQ-C30; [5] clinical and EQ-5D; [6] clinical and FACT-G) were applied in six ML models (logistic regression [LR], decision tree, adaptive boosting, random forest [RF], support vector machines [SVMs], and neural network) to predict admissions, triage events, and chemotherapy changes.
Results
The comprehensive analysis of predictive performances of the six ML models for each feature set in three different methods for handling class imbalance indicated that PROMs improved predictions of all outcomes. RF and SVMs had the highest performance for predicting admissions and changes to chemotherapy in balanced data sets, and LR in imbalanced data set. Balancing data led to the best performance compared with imbalanced data set or data set with balanced train set only.
Conclusion
These results endorsed the view that ML can be applied on PROM data to predict hospital utilization and chemotherapy management. If further explored, this study may contribute to health care planning and treatment personalization. Rigorous comparison of model performance affected by different imbalanced data handling methods shows best practice in ML research
Patient-Centric Approach for Utilising Machine Learning to Predict Health-Related Quality of Life Changes During Chemotherapy
No full textPatients undergoing chemotherapy often experience adverse effects, which can lead to changes in health-related quality of life (HRQOL) and have detrimental effects on patients’ physical and psychological wellbeing. This study aims to apply machine learning (ML) models to patient-reported, clinical, and demographic data to predict changes in physical well-being, social functioning, role functioning, usual activities, and mobility at 6, 12 and 18 weeks from starting chemotherapy. A patient-centric approach is followed as outcome variables were selected after consultation with patients and a clinician, who also was involved in the study design. Logistic regression, random forest, extreme gradient boosting, and multilayer perceptron were developed and their performance of predicting improvement and deterioration in HRQOL was evaluated with accuracy, recall, specificity, and area under the ROC curve (AUC). Model performance was generally better when predicting improvement, with best models giving AUC of 0.904 for predicting mobility improvement at 12 weeks and AUC of 0.898 for predicting usual activities improvement at 18 weeks. The results encourage involving stakeholders in research and support the view that ML can be used to predict outcomes meaningful to patients. They also highlight that although some outcome variables can be valuable for patients, they may not be predicted well by ML models. This study can inform future work on patient-centric ML methods contributing to treatment decisions in oncology
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