Purpose Rewards’ management has long been used as a panacea to promote job satisfaction and labour retention. However, the relationship between these variables is not clearly defined in the real estate industry, due to the scarcity of empirical studies. The purpose of this study is to evaluate the role of both satisfaction with intrinsic rewards (SIR) and satisfaction with extrinsic rewards (SER) on job satisfaction and turnover intention in the real estate industry. Design/methodology/approach Using a sample of 220 employees from the three largest real estate agencies in Portugal, the study analyses a conceptual framework and tests hypotheses by using partial least squares (PLS), along with importance-performance map analysis (IPMA). Findings Results indicate that both SIR and SER have a positive impact on job satisfaction. However, SER has a stronger impact on job satisfaction. Satisfaction with rewards and job satisfaction are negatively related to turnover intention. Job satisfaction mediates the relationship between satisfaction with rewards and turnover intention. Results also show gender and age differences. SIR is more important for women and younger agents. SER has similar importance for men and women, but higher importance for older agents. Research limitations/implications Findings of this study extend the existing literature on rewards satisfaction and turnover intention to the context of the real estate industry. They present a contribution to the current debate on extrinsic vs intrinsic rewards for this particular industry. Practical implications Following the results of this research, real estate managers should consider intrinsic rewards because they also play a role for job satisfaction and turnover intentions. Human resource managers should consider identifying employees’ needs and motivations and then implement adequate strategies to promote their job satisfaction because it plays a mediating role between satisfaction with rewards and turnover intention. Reward strategies should also consider gender and age differences by giving women and younger agents more recognition, responsibilities and other intrinsic rewards because they are important for their job satisfaction. Originality/value Previous studies on real estate agents rewards appear to have only focussed on extrinsic rewards. To the best of the knowledge, this is the first study to analyse the effects of SIR on job satisfaction and turnover intention in the real estate industry. Also, to the best of the knowledge, this study is original in the use of IPMA to detect gender and age differences.

The frequency of AE in ambulatory settings in LA is in the high-frequency range of research focused on the prevalence of AEs. Fifty percent was preventable. This study provides an approach for assessing the frequency and preventability of AE in order to enhance patient safety in LA.

BackgroundOntologies are commonly used to annotate and help process life sciences data. Although their original goal is to facilitate integration and interoperability among heterogeneous data sources, when these sources are annotated with distinct ontologies, bridging this gap can be challenging. In the last decade, ontology matching systems have been evolving and are now capable of producing high-quality mappings for life sciences ontologies, usually limited to the equivalence between two ontologies. However, life sciences research is becoming increasingly transdisciplinary and integrative, fostering the need to develop matching strategies that are able to handle multiple ontologies and more complex relations between their concepts.ResultsWe have developed ontology matching algorithms that are able to find compound mappings between multiple biomedical ontologies, in the form of ternary mappings, finding for instance that “aortic valve stenosis”(HP:0001650) is equivalent to the intersection between “aortic valve”(FMA:7236) and “constricted” (PATO:0001847). The algorithms take advantage of search space filtering based on partial mappings between ontology pairs, to be able to handle the increased computational demands. The evaluation of the algorithms has shown that they are able to produce meaningful results, with precision in the range of 60-92% for new mappings. The algorithms were also applied to the potential extension of logical definitions of the OBO and the matching of several plant-related ontologies.ConclusionsThis work is a first step towards finding more complex relations between multiple ontologies. The evaluation shows that the results produced are significant and that the algorithms could satisfy specific integration needs.

ObjectiveTo establish the prevalence of physical, cognitive and psychiatric disabilities, associated factors and their relationship with the qualities of life of intensive care survivors in Brazil.MethodsA prospective multicenter cohort study is currently being conducted at 10 adult medical-surgical intensive care units representative of the 5 Brazilian geopolitical regions. Patients aged ≥ 18 years who are discharged from the participating intensive care units and stay 72 hours or more in the intensive care unit for medical or emergency surgery admissions or 120 hours or more for elective surgery admissions are consecutively included. Patients are followed up for a period of one year by means of structured telephone interviews conducted at 3, 6 and 12 months after discharge from the intensive care unit. The outcomes are functional dependence, cognitive dysfunction, anxiety and depression symptoms, posttraumatic stress symptoms, health-related quality of life, rehospitalization and long-term mortality.DiscussionThe present study has the potential to contribute to current knowledge of the prevalence and factors associated with postintensive care syndrome among adult intensive care survivors in Brazil. In addition, an association might be established between postintensive care syndrome and health-related quality of life.

2014. The Properties of Property Alignment on the Semantic Web. Ontology alignment is an important step in enabling computers to query and reason across the many linked datasets on the semantic web. This is a difficult challenge because the ontologies underlying different linked datasets can vary in terms of subject area coverage, level of abstraction, ontology modeling philosophy, and even language. The alignment approach presented here centers on string similarity metrics. Nearly all ontology alignment systems use a string similarity metric in one form or another, but it seems that the choice of a particular metric is often arbitrary. We begin this dissertation with the most comprehensive survey to date on the performance of string similarity metrics and string preprocessing strategies for ontology alignment. Based on this work we present practical guidelines for choosing string metrics in the face of different types of ontologies and different alignment goals. Additionally, we show that string similarity metrics alone can perform competitively with state-of-the-art alignment systems on the most popular benchmarks in the field. One of the contributions of our string similarity metric survey is quantification of the difference in performance between aligning classes and aligning properties (relations between classes). Put simply: aligning properties is hard, and existing string similarity metrics are not of great help. We therefore take on the task of developing a new string-based alignment approach that performs better on properties. Unfortunately, evaluating that approach is difficult because the only existing alignment benchmark that includes properties is, in our view, unrealistic since all relations in the reference alignment are presented as completely certain. Human experts do not have this degree of confidence when asked to align an ontology. We therefore present a more nuanced version of this benchmark that we have created through a combination of expert survey and crowdsourcing. We then present our new string-based property alignment system and evaluate its performance on both the current benchmark and our proposed revision. Our property-centric string metric can be configured for either high precision or high recall. The results show a five-fold increase in precision and a doubling of recall over an approach based on the best current string metric. Finally, we apply our system to a real-world test case and analyze the results.

Background Intensive Care Unit (ICU) readmissions represent both a health risk for patients,with increased mortality rates and overall health deterioration, and a financial burden for healthcare facilities. As healthcare became more data-driven with the introduction of Electronic Health Records (EHR), machine learning methods have been applied to predict ICU readmission risk. However, these methods disregard the meaning and relationships of data objects and work blindly over clinical data without taking into account scientific knowledge and context. Ontologies and Knowledge Graphs can help bridge this gap between data and scientific context, as they are computational artefacts that represent the entities of a domain and their relationships to each other in a formalized way. Methods and results We have developed an approach that enriches EHR data with semantic annotations to ontologies to build a Knowledge Graph. A patient’s ICU stay is represented by Knowledge Graph embeddings in a contextualized manner, which are used by machine learning models to predict 30-days ICU readmissions. This approach is based on several contributions: (1) an enrichment of the MIMIC-III dataset with patient-oriented annotations to various biomedical ontologies; (2) a Knowledge Graph that defines patient data with biomedical ontologies; (3) a predictive model of ICU readmission risk that uses Knowledge Graph embeddings; (4) a variant of the predictive model that targets different time points during an ICU stay. Our predictive approaches outperformed both a baseline and state-of-the-art works achieving a mean Area Under the Receiver Operating Characteristic Curve of 0.827 and an Area Under the Precision-Recall Curve of 0.691. The application of this novel approach to help clinicians decide whether a patient can be discharged has the potential to prevent the readmission of $$40\%$$ 40 % of Intensive Care Unit patients, without unnecessarily prolonging the stay of those who would not require it. Conclusion The coupling of semantic annotation and Knowledge Graph embeddings affords two clear advantages: they consider scientific context and they are able to build representations of EHR information of different types in a common format. This work demonstrates the potential for impact that integrating ontologies and Knowledge Graphs into clinical machine learning applications can have.

The source code (of seven ranking algorithms), ground truths and experimental results are available at https://github.com/danielapoliveira/bioont-search-benchmark.

Objective To describe the IMPACTO-MR, a Brazilian nationwide intensive care unit platform study focused on the impact of health care-associated infections due to multidrug-resistant bacteria. Methods We described the IMPACTO-MR platform, its development, criteria for intensive care unit selection, characterization of core data collection, objectives, and future research projects to be held within the platform. Results The core data were collected using the Epimed Monitor System® and consisted of demographic data, comorbidity data, functional status, clinical scores, admission diagnosis and secondary diagnoses, laboratory, clinical, and microbiological data, and organ support during intensive care unit stay, among others. From October 2019 to December 2020, 33,983 patients from 51 intensive care units were included in the core database. Conclusion The IMPACTO-MR platform is a nationwide Brazilian intensive care unit clinical database focused on researching the impact of health care-associated infections due to multidrug-resistant bacteria. This platform provides data for individual intensive care unit development and research and multicenter observational and prospective trials.