This chapter examines two major contentious episodes in post-transition Ukraine: the 2004 Orange Revolution and the 2013–2014 Euromaidan/Revolution of Dignity. I illustrate the interplay between power structures and protest dynamics by focusing on increased salience and transformation of a geopolitical orientation cleavage. Using a toolkit developed by the fields conceptual framework, I show how collective incumbents, challengers, and governance units interacted to alter the power balance within the broader Ukrainian political field. Following the fields approach, the analysis accounts for various collective actors, including activist initiatives and NGOs, counter-protesters, financial-industrial groups, law enforcement agencies, local self-governments, and courts. Among these, political parties deserve special attention, as they added street protests to their usual repertoire early on in Ukraine’s history and often assumed an identity of contentious actors since. This analysis also accounts for the role played by actors in proximate fields in influencing domestic contention. I underline the impact of exogenous shocks on the dynamics of domestic politics by outlining post-Euromaidan political developments, the emergence of a powerful volunteer movement and civic resistance, as Ukraine was hit by the 2014 Russian aggression, which escalated into a full-scale invasion in 2022.
Aim: To explore the experiences of sexual health and intimate relationships in women with lipedema.
Design: A qualitative interview study.
Methods: Sixteen women with lipedema recruited from a national lipedema association group in Sweden participated. Data were collected between October 2023 and February 2024 and analysed using content analysis.
Results: Four main categories were identified. Being burdened by body shame highlighted the women's body dissatisfaction, difficulties in self-acceptance and shame when being naked in front of their partners. Being hindered by an aching and heavy body captured the experiences of bodily pain and heaviness in intimate situations. Being torn between desire and avoidance revealed that the women longed for intimacy but adopted different strategies to avoid it. Being affected by what is said and what is left unsaid showed the role of the partner's approach as well as the strain of not discussing sexual problems with partners or healthcare
providers.
Conclusion: Women with lipedema experience physical, psychological and relational challenges in sexual life and intimacy. Still, these challenges remain unaddressed in healthcare settings.
Implications for the Profession and Patient Care: Nurses and midwives should, in their unique role in encountering women throughout their lifespans, address and invite dialogues on sexual-related concerns of women affected by lipedema.
Impact: The study findings provide insights into how lipedema affects women's sexual health and intimate relationships and offer new knowledge that can be applied when planning targeted interventions to promote the sexual health and well-being of women with lipedema.
Reporting Method: Adherence to the EQUATOR guidelines and COREQ checklist.
Patient or Public Contribution: Two patient representatives from a lipedema association group contributed to this study by distributing study information to their members.
The Guided Imagery technique is reported to be used by therapists all over the world in order to increase the comfort of patients suffering from a variety of disorders from mental to oncology ones and proved to be successful in numerous of ways. Possible support for the therapists can be estimation of the time at which subject goes into deep relaxation. This paper presents the results of the investigations of a cohort of 26 students exposed to Guided Imagery relaxation technique and mental task workloads conducted with the use of dense array electroencephalographic amplifier. The research reported herein aimed at verification whether it is possible to detect differences between those two states and to classify them using deep learning methods and recurrent neural networks such as EEGNet, Long Short-Term Memory-based classifier, 1D Convolutional Neural Network and hybrid model of 1D Convolutional Neural Network and Long Short-Term Memory. The data processing pipeline was presented from the data acquisition, through the initial data cleaning, preprocessing and postprocessing. The classification was based on two datasets: one of them using 26 so-called cognitive electrodes and the other one using signal collected from 256 channels. So far there have not been such comparisons in the application being discussed. The classification results are presented by the validation metrics such as: accuracy, recall, precision, F1-score and loss for each case. It turned out that it is not necessary to collect signals from all electrodes as classification of the cognitive ones gives the results similar to those obtained for the full signal and extending input to 256 channels does not add much value. In Disscussion there were proposed an optimal classifier as well as some suggestions concerning the prospective development of the project.
