INDIVIDUAL PREFERENCES IN SECURITY RISK DECISION MAKING: AN EXPLORATORY STUDY UNDER SECURITY PROFESSIONALS

AbstractMaking decisions regarding data and the overall credibility of research constitutes research data governance. In this paper, we present results of an exploratory study of the stakeholders of research data governance. The study was conducted among individuals who work in academic and research institutions in the US, with the goal of understanding what entities are perceived as making decisions regarding data and who researchers believe should be responsible for governing research data. Our results show that there is considerable diversity and complexity across stakeholders, both in terms of who they are and their ideas about data governance. To account for this diversity, we propose to frame research data governance in the context of polycentric governance of a knowledge commons. We argue that approaching research data from the commons perspective will allow for a governance framework that can balance the goals of science and society, allow us to shift the discussion toward protection from enclosure and knowledge resilience, and help to ensure that multiple voices are included in all levels of decision-making.

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The science of bureaucracy: Risk decision making and the US Environmental Protection Agency, DavidDemortain, MIT Press, Cambridge, MA, 2020. 453 pp. $55.00 (paper)

Governance ◽

10.1111/gove.12621 ◽

2021 ◽

Author(s):

Andreas Eriksen

Keyword(s):

Decision Making ◽

Environmental Protection ◽

Environmental Protection Agency ◽

Protection Agency ◽

The Us ◽

Risk Decision

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307 - Exploratory Study on Healthcare Decision-Making Capacity Assessment

International Psychogeriatrics ◽

10.1017/s1041610220002070 ◽

2020 ◽

Vol 32 (S1) ◽

pp. 65-65

Author(s):

Ana Saraiva Amaral ◽

Rosa Marina Afonso ◽

Mário R. Simões ◽

Sandra Freitas

Keyword(s):

Decision Making ◽

Content Analysis ◽

Focus Groups ◽

Exploratory Study ◽

Assessment Instrument ◽

Funding Agency ◽

Capacity Assessment ◽

Healthcare Decision ◽

Healthcare Decision Making ◽

Decision Making Capacity

Mild cognitive impairment (MCI) and Alzheimer’s Disease (AD) prevalence is expected to continue to increase, due to the population ageing. MCI and AD may impact patients’ decision-making capacities, which should be assessed through the disease course. These medical conditions can affect the various areas of decision-making capacity in different ways. Decision-making capacity in healthcare is particularly relevant among this population. Elders often suffer from multimorbidity and are frequently asked to make healthcare decisions, which can vary from consenting a routine diagnostic procedure to decide receiving highly risk treatments.To assess this capacity in elders with MCI or AD, we developed the Healthcare Decision-Making Capacity Assessment Instrument (IACTD-CS - Instrumento de Avaliação da Capacidade de Tomada de Decisão em Cuidados de Saúde). This project is funded by Portuguese national funding agency for science, research and technology, FCT (SFRH/BD/139344/2018). IACTD-CS was developed based on Appelbaum and Grisso four abilities model, literature review and review of international assessment instruments. After IACTD-CS first version development, an exploratory study with focus groups was conducted. This study included focus groups with healthcare professionals and nursing homes’ professionals.The focus groups main goals were: 1) understand the participants perception regarding healthcare decision-making capacity, 2) distinguish relevant aspects of decision-making, 3) discuss the abilities and items included in IACTD-CS and 4) identify new aspects or items to be added to IACTD-CS. A content analysis of the focus groups results, with resource to MAXQDA, was conducted afterwards. This exploratory study allowed to identify professionals’ perceptions on healthcare decision-making and its results were a significant contribute to IACTD-CS development. The proposed communication aims to describe the methodology used and present the results of content analysis.

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