• Background Lack of communication from healthcare providers contributes to the anxiety and distress reported by patients’ families after a patient’s death in the intensive care unit.• Objective To obtain a detailed picture of the experiences of family members during the hospitalization and death of a loved one in the intensive care unit.• Methods A qualitative study with 4 focus groups was used. All eligible family members from 8 intensive care units were contacted by telephone; 8 members agreed to participate.• Results The experiences of the family members resembled a vortex: a downward spiral of prognoses, difficult decisions, feelings of inadequacy, and eventual loss despite the members’ best efforts, and perhaps no good-byes. Communication, or its lack, was a consistent theme. The participants relied on nurses to keep informed about the patients’ condition and reactions. Although some participants were satisfied with this information, they wished for more detailed explanations of procedures and consequences. Those family members who thought that the best possible outcome had been achieved had had a physician available to them, options for treatment presented and discussed, and family decisions honored.• Conclusions Uncertainty about the prognosis of the patient, decisions that families make before a terminal condition, what to expect during dying, and the extent of a patient’s suffering pervade families’ end-of-life experiences in the intensive care unit. Families’ information about the patient is often lacking or inadequate. The best antidote for families’ uncertainty is effective communication.

BACKGROUND: With much attention being focused on how patients die and whether or not they are provided appropriate care, the care of dying patients in intensive care units must be described and improved. OBJECTIVES: To describe end-of-life care in intensive care units as perceived by critical care nurses who have taken care of dying patients. METHODS: A semistructured interview guide was developed and revised after pretesting in a focus group of faculty clinicians with extensive, recent experience in intensive care. Four focus groups were held with randomly selected nurses from 4 intensive care units in 2 hospitals; participants had 2 years or more of experience and were working half-time or more. Tapes from each focus group were transcribed and reviewed by the investigators before the subsequent group met. Category labels were developed, and topics and themes were determined. RESULTS: "Good" end-of-life care in the intensive care unit was described as ensuring that the patient is as pain-free as possible and that the patient's comfort and dignity are maintained. Involvement of the patient's family is crucial. A clear, accurate prognosis and continuity of care also are important. Switching from curative care to comfort care is awkward. CONCLUSIONS: Disagreement among patients' family members or among caregivers, uncertainty about prognosis, and communication problems further complicate end-of-life care in intensive care units. Changes in the physical environment, education about end-of-life care, staff support, and better communication would improve care of dying patients and their families.

We explain how to use elicited priors in Bayesian political science research. These are a form of prior information produced by previous knowledge from structured interviews with subjective area experts who have little or no concern for the statistical aspects of the project. The purpose is to introduce qualitative and area-specific information into an empirical model in a systematic and organized manner in order to produce parsimonious yet realistic implications. Currently, there is no work in political science that articulates elicited priors in a Bayesian specification. We demonstrate the value of the approach by applying elicited priors to a problem in judicial comparative politics using data and elicitations we collected in Nicaragua.As quantitative political research becomes increasingly sophisticated, the more complex, but more capable, Bayesian approach is likely to grow in popularity. The Bayesian inferential engine is a coherent set of axioms that converts prior information to posterior evidence by conditioning on observed data. Thus, stipulating prior distributions for unknown quantities is a requirement, and this requirement has been a long-standing source of controversy. Bayesian statistics provide a number of ways to define prior information, and the strength of these prior assertions can vary considerably within the same inferential framework.Recent Bayesian work in fields other than political science has exploited the elicited prior as a means of drawing information from subject-area experts with the goal of constructing a probability structure that reflects their specific qualitative knowledge, and perhaps experiential intuition, about the studied effects. These informed priors derive their name from the way in which the information is elicited from nonstatisticians who have a great deal of information about the substantive question but are not involved in the model construction process. Such experts can be physicians, policy-makers, theoretical economists, historians, previous study participants, outside experts, politicians, community leaders, and others.

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