Abstract
In a recent paper (10.1007/s11192-020-03386-9) we proposed a model to estimate the citations of an article in a database (Scopus/Web of Science) in which it is not indexed using the percentile rank of the database (Web of Science/Scopus) in which it is indexed. In this study we supplement the previous work with three advances: (1) by using 15 different research fields, corresponding to over 1 million papers, since we previously used only four fields; (2) by measuring the agreement between the percentile ranks in both databases using Lin's concordance correlation coefficient, since this coefficient has not been used previously to measure this agreement, but as a test with a sample of 15,400 papers to compare the actual and estimated number of citations; and (3) by using a robust data cleaning procedure. The results revealed a substantial concordance between percentile ranks of papers indexed in these two databases in all the research fields studied, and that this concordance is even stronger for high percentile values. This level of concordance suggests that we can consider the percentile of a paper in a database in which it is not indexed as being equal to the percentile of this paper in a database in which it is indexed. In other words, we increased the reliability of our previous conclusions that the percentile rank can be used as a citation database-normalization. The results of this study contribute to improve the use of citation counts in bibliometric studies, and to calculate research indicators when we need to use both bibliographic databases.

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Abstract
Patients with end-stage renal disease should be educated and trained to take care of their own arteriovenous fistula (AVF) with the purpose of developing self-care behaviors concerning vascular access. This was a prospective and observational study. We designed this research to identify clinically meaningful self-care behavior profiles in hemodialysis (HD) patients, and it was carried out in a private dialysis unit in the Lisbon region, Portugal, involving 101 patients. The proportion of male patients was 66.3%, the mean age was 60.9 years, and the frequency of self-care behaviors was 71%. Cluster analysis based on the subscale scores grouped patients in two clusters named "moderate self-care" and "high self-care." Those profiles exhibit significant differences concerning gender, education, employment, dialysis vintage, AVF duration, and information on care with the AVF. Identification of self-care-behavior profiles in HD patients with AVF enables one to adjust education programs to the patients' characteristics.

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Abstract
Objective: to analyze the factorial structure of the Perception Scale of Nursing Activities that Contribute to the Quality of Care. Method: a methodological study with 3,451 nurses from 36 Portuguese hospitals. In addition to carrying out confirmatory factorial analysis, Cronbach’s alpha and composite reliability were used to assess the reliability of the obtained factorial model. Results: the factorial weights of the solution found were mostly high; the values of the model’s adjustment indexes were reasonable; Cronbach’s alpha was elevated for the entire scale and five dimensions, being acceptable in only one dimension. The composite reliability was also high in five dimensions, except for one, considered acceptable. All activities showed high individual reliability. Conclusion: Compared to the original scale, the identified factorial model contemplates six dimensions and not seven, producing a reliable and valid scale, which can be applied in the hospital context.

Abstract
Nuisance or unsolicited calls and instant messages come at any time in a variety of different ways. These calls would not only exasperate recipients with the unwanted ringing, impacting their productivity, but also lead to a direct financial loss to users and service providers. Telecommunication Service Providers (TSPs) often employ standalone detection systems to classify call originators as spammers or non-spammers using their behavioral patterns. These approaches perform well when spammers target a large number of recipients of one service provider. However, professional spammers try to evade the standalone systems by intelligently reducing the number of spam calls sent to one service provider, and instead distribute calls to the recipients of many service providers. Naturally, collaboration among service providers could provide an effective defense, but it brings the challenge of privacy protection and system resources required for the collaboration process. In this paper, we propose a novel decentralized collaborative system named privy for the effective blocking of spammers who target multiple TSPs. More specifically, we develop a system that aggregates the feedback scores reported by the collaborating TSPs without employing any trusted third party system, while preserving the privacy of users and collaborators. We evaluate the system performance of privy using both the synthetic and real call detail records. We find that privy can correctly block spammers in a quicker time, as compared to standalone systems. Further, we also analyze the security and privacy properties of the privy system under different adversarial models.