Dataset Information

Moral decision-making and support for safety procedures amid the COVID-19 pandemic.

ABSTRACT: Given that individual differences influence virus-mitigating behaviors and the COVID-19 pandemic posed new moral dilemmas for individuals to resolve, across three studies (N = 704), we assessed how masculine honor beliefs (MHB), beliefs in pure good (BPG), evil (BPE), and the dark triad (DT) influence COVID-19 moral decision-making. Specifically, we analyzed moral decision-making at the microlevel (i.e., individual- and familial-level; Study 1), in decisions with (hypothetical) life-or-death consequences (Study 2), and at the macrolevel (i.e., nationwide virus-mitigation efforts; Study 3). In all studies, participants completed the four individual difference scales and rated their pandemic attitudes on Likert-type agreement scales, and resolved various moral dilemmas in Studies 2 and 3. Consistent with our hypotheses, individuals reported more virus-mitigation efforts in order to protect their families than themselves. In terms of hypothetical life-or-death and nationwide decisions, MHB, BPE, and the DT predicted more confidence and social motivations, whereas BPG predicted more distress. This research has implications for moral decision-making at varying degrees of severity during the COVID-19 pandemic.

SUBMITTER: Schiffer AA

PROVIDER: S-EPMC7847405 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Moral decision-making and support for safety procedures amid the COVID-19 pandemic.

Schiffer Ashley A AA O'Dea Conor J CJ Saucier Donald A DA

Personality and individual differences 20210131

Given that individual differences influence virus-mitigating behaviors and the COVID-19 pandemic posed new moral dilemmas for individuals to resolve, across three studies (<i>N</i> = 704), we assessed how masculine honor beliefs (MHB), beliefs in pure good (BPG), evil (BPE), and the dark triad (DT) influence COVID-19 moral decision-making. Specifically, we analyzed moral decision-making at the microlevel (i.e., individual- and familial-level; Study 1), in decisions with (hypothetical) life-or-de ...[more]

PMID: 33551530

Similar Datasets

Project description:Background and Objectives: Overweight/obesity puts individuals at greater risk for COVID-19 progression and mortality. We aimed to evaluate the impact of overweight/obesity on oxygen (O2) requirement outcomes of male and female kidney transplant recipients (KTRs) during the COVID-19 pandemic. Materials and Methods: We conducted a retrospective analysis of a cohort of KTRs diagnosed with COVID-19. Participants were stratified based on BMI categories, and data on the need for O2 therapy outcome were collected and analyzed separately for male and female KTRs. Results: In total, 284 KTRs (97 males and 187 females) were included in the study. Overweight/obesity was observed in 60.6% of male KTRs and 71% of female KTRs. Strikingly, overweight/obese women had a significantly higher requirement for supplemental O2 (63.3% vs. 41.7%, OR = 2.45, p = 0.03), particularly among older individuals (OR = 1.05, p = 0.04), smokers (OR = 4.55, p = 0.03), those with elevated lactate dehydrogenase (LDH) levels (OR = 1.01, p = 0.006), and those with lower admission and basal estimated glomerular filtration rate (eGFR) levels. Within this cohort, the necessity for O2 supplementation was correlated with more unfavorable outcomes. These included heightened mortality rates, transfers to the intensive care unit, employment of invasive mechanical ventilation, and the emergence of acute kidney injury requiring hemodialysis. On the other hand, although overweight/obese male KTRs had a higher prevalence of hypertension and higher fasting blood glucose levels, no significant association was found with COVID-19-related outcomes when compared to lean male KTRs. Conclusions: Overweight/obesity is highly prevalent in KTRs, and overweight/obese women demonstrated a higher need for supplemental O2. Therefore, the early identification of factors that predict a worse outcome in overweight/obese female KTRs affected by COVID-19 contributes to risk stratification and guides therapeutic decisions.

Project description:BackgroundThe COVID-19 pandemic affected healthcare systems worldwide. Predictive models developed by Artificial Intelligence (AI) and based on timely, centralized and standardized real world patient data could improve management of COVID-19 to achieve better clinical outcomes. The objectives of this manuscript are to describe the structure and technologies used to construct a COVID-19 Data Mart architecture and to present how a large hospital has tackled the challenge of supporting daily management of COVID-19 pandemic emergency, by creating a strong retrospective knowledge base, a real time environment and integrated information dashboard for daily practice and early identification of critical condition at patient level. This framework is also used as an informative, continuously enriched data lake, which is a base for several on-going predictive studies.MethodsThe information technology framework for clinical practice and research was described. It was developed using SAS Institute software analytics tool and SAS® Vyia® environment and Open-Source environment R ® and Python ® for fast prototyping and modeling. The included variables and the source extraction procedures were presented.ResultsThe Data Mart covers a retrospective cohort of 5528 patients with SARS-CoV-2 infection. People who died were older, had more comorbidities, reported more frequently dyspnea at onset, had higher d-dimer, C-reactive protein and urea nitrogen. The dashboard was developed to support the management of COVID-19 patients at three levels: hospital, single ward and individual care level.InterpretationThe COVID-19 Data Mart based on integration of a large collection of clinical data and an AI-based integrated framework has been developed, based on a set of automated procedures for data mining and retrieval, transformation and integration, and has been embedded in the clinical practice to help managing daily care. Benefits from the availability of a Data Mart include the opportunity to build predictive models with a machine learning approach to identify undescribed clinical phenotypes and to foster hospital networks. A real-time updated dashboard built from the Data Mart may represent a valid tool for a better knowledge of epidemiological and clinical features of COVID-19, especially when multiple waves are observed, as well as for epidemic and pandemic events of the same nature (e. g. with critical clinical conditions leading to severe pulmonary inflammation). Therefore, we believe the approach presented in this paper may find several applications in comparable situations even at region or state levels. Finally, models predicting the course of future waves or new pandemics could largely benefit from network of DataMarts.

Dataset Information

Moral decision-making and support for safety procedures amid the COVID-19 pandemic.

Publications

Moral decision-making and support for safety procedures amid the COVID-19 pandemic.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets