Paulo Teles

Abstract
We investigate the fuzzy clustering of interval time series using wavelet variances and covariances; in particular, we use a fuzzy c-medoids clustering algorithm. Traditional hierarchical and non-hierarchical clustering methods lead to the identification of mutually exclusive clusters whereas fuzzy clustering methods enable the identification of overlapping clusters, implying that one or more series could belong to more than one cluster simultaneously. An interval time series (ITS) which arises when interval-valued observa-tions are recorded over time is able to capture the variability of values within each interval at each time point. This is in contrast to single-point information available in a classical time series. Our main contribution is that by combining wavelet analysis, interval data analysis and fuzzy clustering, we are able to capture information which would otherwise have not been contemplated by the use of traditional crisp clustering methods on classical time series for which just a single value is recorded at each time point. Through simulation studies, we show that under some circumstances fuzzy c-medoids clustering performs better when applied to ITS than when it is applied to the corresponding traditional time series. Applications to exchange rates ITS and sea-level ITS show that the fuzzy clustering method reveals different and more meaningful results than when applied to associated single-point time series.

Abstract
We investigate the fuzzy clustering of interval time series using wavelet variances and covariances; in particular, we use a fuzzy c-medoids clustering algorithm. Traditional hierarchical and non-hierarchical clustering methods lead to the identification of mutually exclusive clusters whereas fuzzy clustering methods enable the identification of overlapping clusters, implying that one or more series could belong to more than one cluster simultaneously. An interval time series (ITS) which arises when interval-valued observa-tions are recorded over time is able to capture the variability of values within each interval at each time point. This is in contrast to single-point information available in a classical time series. Our main contribution is that by combining wavelet analysis, interval data analysis and fuzzy clustering, we are able to capture information which would otherwise have not been contemplated by the use of traditional crisp clustering methods on classical time series for which just a single value is recorded at each time point. Through simulation studies, we show that under some circumstances fuzzy c-medoids clustering performs better when applied to ITS than when it is applied to the corresponding traditional time series. Applications to exchange rates ITS and sea-level ITS show that the fuzzy clustering method reveals different and more meaningful results than when applied to associated single-point time series.

Abstract
BackgroundThe COVID-19 pandemic reinforced the need to invest in nursing practice environments and health institutions were led to implement several changes. In this sense, this study aimed to analyze the impact of the changes that occurred in nursing practice environments between the first and fourth critical periods of the pandemic.MethodsQuantitative, observational study, conducted in a University Hospital, with the participation of 713 registered nurses. Data were collected through a questionnaire with sociodemographic and professional characterization and the Scale for the Environments Evaluation of Professional Nursing Practice, applied at two different points in time: from 1 to 30 June 2020 and from 15 August to 15 September 2021. Data were processed using descriptive and inferential statistics.ResultsOverall, the pandemic had a positive impact on nursing practice environments. However, the Process component remained favourable to quality of care, while the Structure and Outcome components only moderately favourable. Nurses working in Medicine Department services showed lower scores in several dimensions of the Structure, Process and Outcome components. On the other hand, nurses working in areas caring for patients with COVID-19 showed higher scores in several dimensions of the Structure, Process and Outcome components.ConclusionsThe pandemic had a positive impact on various dimensions of nursing practice environments, which denotes that regardless of the adversities and moments of crisis that may arise, investment in work environments will have positive repercussions.However, more investment is needed in Medicine Department services, which have historically been characterised by high workloads and structural conditions that make it difficult to promote positive and sustainable workplaces.

Abstract
The COVID-19 pandemic has imposed challenges to health systems and institutions, which had to quickly create conditions to meet the growing health needs of the population. Thus, this study aimed to assess the impact of COVID-19 on professional nursing practice environments and to identify the variables that affected their quality. Quantitative, observational study, conducted in 16 Portuguese hospitals, with 1575 nurses. Data were collected using a questionnaire and participants responded to two different moments in time: the pre-pandemic period and after the fourth critical period of COVID-19. The pandemic had a positive impact on the Structure and Outcome components, and a negative trend in the Process component. The variables associated with the qualification of the components and their dimensions were predominantly: work context, the exercise of functions in areas of assistance to COVID-19 patients, length of professional experience and length of experience in the service. The investment in professional practice environments impacted the improvement of organizational factors, supporting the development of nurses’ work towards the quality of care. However, it is necessary to invest in nurses’ participation, involvement and professional qualifications, which are aspects strongly dependent on the institutions’ management strategies. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Abstract