Aims: To explore decision control preferences and decisional conflicts and to analyse their association among the surrogate decision makers in the intensive care unit.
Design: The study carried out a cross-sectional survey among the surrogates.
Methods: The participants were 115 surrogate decision makers of critical patients, from August to September 2019. A Chi-squared test and logistic regression were used to assess decision control preferences and decisional conflicts, and Spearman's rank correlation coefficient was employed to examine their association.
Results: Of the 115 surrogate decision makers, 51.3% preferred a collaborative role, and 63.48% were somewhat unsure about making decisions. Logistic regression analysis identified decision control preferences was associated with surrogates' age, education level, and personality traits, while decisional conflicts was associated with surrogates' age, education level, character, medical expense burden, and Acute Physiology and Chronic Health Evaluation-II score. Cohen's kappa statistics showed a bad concordance of decision-making expectations and actuality, with kappa values of 0.158 (p < .05). Wherein surrogates who experienced discordance between their preferred and actual roles, have relatively higher decisional conflicts.
Conclusion: This study identified individual differences of surrogate decision makers in decision control preferences and decisional conflicts. These results imply that incorporation of the individual decision preferences and communication styles into care plans is an important first step to develop high quality decision support.
Impact: This research is a contribution to the limited study on decision control preferences and decisional conflicts among surrogate decision makers of critically ill patients. Moreover based on the investigation of understanding the status and related factors of decision preferences and decisional conflicts set the stage for developing effective decision support interventions.
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http://dx.doi.org/10.1111/jan.14451 | DOI Listing |
BMC Health Serv Res
January 2025
Department of School and Social Adaptation Studies, Faculty of Education, Université de Sherbrooke, Sherbrooke, Canada.
Background: The COVID-19 pandemic necessitated the rapid availability of evidence to respond in a timely manner to the needs of practice settings and decision-makers in health and social services. Now that the pandemic is over, it is time to put in place actions to improve the capacity of systems to meet knowledge needs in a situation of crisis. The main objective of this project was thus to develop an action plan for the rapid syntheses of evidence in times of health crisis in Quebec (Canada).
View Article and Find Full Text PDFJ Public Health Policy
January 2025
Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia.
Evidence-informed policymaking emphasizes that policy decisions should be informed by the best available evidence from research and follow a systematic and transparent approach. For public health policymaking we can learn from existing practices of transparent, evidence-informed decision-making for clinical practice, medicines, and medical technology. We review existing evidence-to-decision frameworks, as well as frameworks and theories for policymaking to address the political dimension of policymaking, and use this analysis to propose an integrated framework to guide evidence-informed policymaking.
View Article and Find Full Text PDFSci Rep
January 2025
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences (CAS), Beijing, 100101, China.
Flash flood susceptibility mapping is essential for identifying areas prone to flooding events and aiding decision-makers in formulating effective prevention measures. This study aims to evaluate the flash flood susceptibility in the Yarlung Tsangpo River Basin (YTRB) using multiple machine learning (ML) models facilitated by the H2O automated ML platform. The best-performing model was used to generate a flash flood susceptibility map, and its interpretability was analyzed using the Shapley Additive Explanations (SHAP) tree interpretation method.
View Article and Find Full Text PDFAccid Anal Prev
January 2025
USDOT Center for Advanced Multimodal Mobility Solutions and Education, United States; Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, United States. Electronic address:
Speeding crashes remain high injury severities after the stay-at-home order in California, highlighting a need for further investigation into the fundamental cause of this increment. To systematically explore the temporal impacts of the stay-at-home order on speeding behaviors and the corresponding crash-injury outcomes, this study utilizes California-reported single-vehicle speeding crashes on freeways (access-controlled) and non-freeways (non-access-controlled) before, during, and after the order. Significant injury factors and in-depth heterogeneity across observations are identified by random parameter logit models with heterogeneity in means and variances.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Virginia Commonwealth University, Richmond, VA, United States.
Health care is undergoing a "revolution," where patients are becoming consumers and armed with apps, consumer review scores, and, in some countries, high out-of-pocket costs. Although economic analyses and health technology assessment (HTA) have come a long way in their evaluation of the clinical, economic, ethical, legal, and societal perspectives that may be impacted by new technologies and procedures, these approaches do not reflect underlying patient preferences that may be important in the assessment of "value" in the current value-based health care transition. The major challenges that come with the transformation to a value-based health care system lead to questions such as "How are economic analyses, often the basis for policy and reimbursement decisions, going to switch from a societal to an individual perspective?" and "How do we then assess (economic) value, considering individual preference heterogeneity, as well as varying heuristics and decision rules?" These challenges, related to including the individual perspective in cost-effectiveness analysis (CEA), have been widely debated.
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