Processo de gestão de millennials enquanto profissionais de tecnologias de informação em regime de outsourcing

O outsourcing de recursos humanos na área das tecnologias de informação tem sido uma tendência nas últimas décadas tornando-se a tendência dominante no cenário contemporâneo do outsourcing. Os millennials serão em breve, globalmente, a maioria da força de trabalho tendo um modo particular de se relacionarem com as organizações e com o trabalho em si como nenhuma geração anterior o fez. As tecnologias de informação continuam a empregar cada vez mais recursos humanos existindo uma procura crescente e uma escassez de recursos competentes. É por isso importante repensar os processos de gestão de recursos humanos atuais e desenhar um novo processo estratégico adequado e capaz de acomodar e antecipar as necessidades dos gestores e de acompanhar o desenvolvimento desta geração enquanto profissionais de tecnologias de informação. O objetivo desta investigação é a intersecção e análise das dimensões de Tecnologias de Informação e Comunicações, os millennials, a Gestão de Recursos Humanos, o outsourcing e as organizações que, através da metodologia de investigação Design Science Research, irá permitir criar novos processos para a gestão de millennials enquanto profissionais de Tecnologias de Informação em regime de outsourcing.The Information Technologies Outsourcing model has been a trend in recent decades, becoming the dominant trend in contemporary outsourcing scenarios. Millennials will soon, globally, be the majority of the workforce, having a particular way of relating to organizations and to work itself as no previous generation did. Information technologies continue to employ more and more human resources, having an increasing demand and a shortage of competent resources. It is therefore important to rethink current human resource management models and design a new strategic and appropriate model to accommodate and anticipate the needs of managers and monitor the development of this generation as Information Technology professionals. The intersection and analysis of the Information and Communications Technologies, millennials, Human Resource Management, outsourcing and organizations is the objective of this study which, using the Design Science Research methodology, will allow to create new models for the management of millennials as Information Technologies Outsourcing professionals

A systematic review

França, T. J. F., Mamede, H. S., & Dos Santos, V. D. (2020). Managing millennials as outsourced information technology professionals: A systematic review. In Proceedings of the 13th IADIS International Conference ICT, Society and Human Beings 2020, ICT 2020 and Proceedings of the 6th IADIS International Conference Connected Smart Cities 2020, CSC 2020 and Proceedings of the 17th IADIS International Conference Web Based Communities and Social Media 2020, WBC 2020 - Part of the 14th Multi Conference on Computer Science and Information Systems, MCCSIS 2020 (pp. 3-10). (Proceedings of the 13th IADIS International Conference ICT, Society and Human Beings 2020, ICT 2020 and Proceedings of the 6th IADIS International Conference Connected Smart Cities 2020, CSC 2020 and Proceedings of the 17th IADIS International Conference Web Based Communities and Social Media 2020, WBC 2020 - Part of the 14th Multi Conference on Computer Science and Information Systems, MCCSIS 2020). IADIS Press.The Information Technology Outsourcing (ITO) model has been a trend in recent decades, becoming the dominant trend in contemporary outsourcing scenario. Millennials will soon, globally, be the majority of the workforce, having a particular way of relating to organizations and to work itself as no previous generation did. Information technologies continue to employ more and more human resources, having an increasing demand and a shortage of competent resources. It is therefore important to rethink current Human Resources Management (HRM) models and design a new strategic and appropriate model to accommodate and anticipate the needs of managers and monitor the development of this generation as Information Technology (IT) professionals. The intersection and analysis of the Information and Communications Technologies (ICT), millennials, Human Resource Management, outsourcing and organizations is the objective of this study, to identify the most relevant articles regarding millennials as outsourced IT professionals.publishersversionpublishe

Artificial intelligence applied to potential assessment and talent identification in an organisational context

França, T. J. F., São Mamede, J. H. P., Barroso, J. M. P., & Santos, V. M. P. D. D. (2023). Artificial intelligence applied to potential assessment and talent identification in an organisational context. Heliyon, 9(4), 1-25. [e14694]. https://doi.org/10.1016/j.heliyon.2023.e14694Our study provides valuable insights into the relationship between artificial intelligence (AI) and Human Resource Management (HRM). We have minimised bias and ensured reliable findings by employing a systematic literature review and the PRISMA statement. Our comprehensive synthesis of the studies included in this research, along with a bibliometric analysis of articles, journals, indexes, authors' affiliations, citations, keyword co-occurrences, and co-authorship analysis, has produced robust results. The discussion of our findings focuses on critical areas of interest, such as AI and Talent, AI Bias, Ethics and Law, and their impact on Human Resource (HR) management. Our research highlights the recognition by organisations of the importance of talent management in achieving a competitive advantage as higher-level skills become increasingly necessary. Although some HR managers have adopted AI technology for talent acquisition, our study reveals that there is still room for improvement. Our study is in line with previous research that acknowledges the potential for AI to revolutionise HR management and the future of work. Our findings emphasise the need for HR managers to be proactive in embracing technology and bridging the technological, human, societal, and governmental gaps. Our study contributes to the growing body of AI and HR management knowledge, providing essential insights and recommendations for future research. The importance of our study lies in its focus on the role of HR in promoting the benefits of AI-based applications, thereby creating a larger body of knowledge from an organisational perspective.publishersversionpublishe

Geoeconomic variations in epidemiology, ventilation management, and outcomes in invasively ventilated intensive care unit patients without acute respiratory distress syndrome: a pooled analysis of four observational studies

Background: Geoeconomic variations in epidemiology, the practice of ventilation, and outcome in invasively ventilated intensive care unit (ICU) patients without acute respiratory distress syndrome (ARDS) remain unexplored. In this analysis we aim to address these gaps using individual patient data of four large observational studies. Methods: In this pooled analysis we harmonised individual patient data from the ERICC, LUNG SAFE, PRoVENT, and PRoVENT-iMiC prospective observational studies, which were conducted from June, 2011, to December, 2018, in 534 ICUs in 54 countries. We used the 2016 World Bank classification to define two geoeconomic regions: middle-income countries (MICs) and high-income countries (HICs). ARDS was defined according to the Berlin criteria. Descriptive statistics were used to compare patients in MICs versus HICs. The primary outcome was the use of low tidal volume ventilation (LTVV) for the first 3 days of mechanical ventilation. Secondary outcomes were key ventilation parameters (tidal volume size, positive end-expiratory pressure, fraction of inspired oxygen, peak pressure, plateau pressure, driving pressure, and respiratory rate), patient characteristics, the risk for and actual development of acute respiratory distress syndrome after the first day of ventilation, duration of ventilation, ICU length of stay, and ICU mortality. Findings: Of the 7608 patients included in the original studies, this analysis included 3852 patients without ARDS, of whom 2345 were from MICs and 1507 were from HICs. Patients in MICs were younger, shorter and with a slightly lower body-mass index, more often had diabetes and active cancer, but less often chronic obstructive pulmonary disease and heart failure than patients from HICs. Sequential organ failure assessment scores were similar in MICs and HICs. Use of LTVV in MICs and HICs was comparable (42·4% vs 44·2%; absolute difference -1·69 [-9·58 to 6·11] p=0·67; data available in 3174 [82%] of 3852 patients). The median applied positive end expiratory pressure was lower in MICs than in HICs (5 [IQR 5-8] vs 6 [5-8] cm H2O; p=0·0011). ICU mortality was higher in MICs than in HICs (30·5% vs 19·9%; p=0·0004; adjusted effect 16·41% [95% CI 9·52-23·52]; p<0·0001) and was inversely associated with gross domestic product (adjusted odds ratio for a US$10 000 increase per capita 0·80 [95% CI 0·75-0·86]; p<0·0001). Interpretation: Despite similar disease severity and ventilation management, ICU mortality in patients without ARDS is higher in MICs than in HICs, with a strong association with country-level economic status

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use