This paper looks at the implications of contemporary work in philosophy of science for nursing science. Early work on the nature of theories in nursing was strongly influenced by logical empiricism, and this influence remains even long after nurse scholars have come to reject logical empiricism as an adequate philosophy of science. Combined with the need to establish nursing as an autonomous profession, nursing theory's use of logical empiricism has led to serious conceptual problems. Philosophers of science have also rejected (...) many of the central tenets of logical empiricism, including its focus on the logical justification of theories and the idea that science is, or should be, unified. Instead, there has been an increasing focus on the practice of science, which in turn has led to a pluralist understanding of science that emphasizes the construction of scientific models that are appropriate for certain purposes or in certain contexts. I suggest that this approach to philosophy of science may provide better resources for nursing science. (shrink)

Diagnostic frameworks are essential to many scientific and technological activities and clinical practice. This study examines the main fundamental aspects of such frameworks. The three components required for all diagnoses are identified and examined, i.e. their normative dimension, temporal nature and structure, and teleological perspective.The normative dimension of a diagnosis is based on (1) epistemic values when associated with Hempel's inductive risk concerning the balance between false‐positive and false‐negative outcomes, leading to probabilistic judgements; and (2) non‐epistemic values when related to (...) ideas such as well‐being, normality, illness, etc, as idealized norms or ideal points of reference.It should be noted that medical diagnoses match the three necessary components, while some essential diagnostic frameworks – the taxonomies of Gordon and NANDA – in nursing lack some components. The main lack is normative as the most popular frameworks in nursing diagnosis seem to be descriptions of observed reality rather than normative and value‐based judgements in which both epistemic and non‐epistemic values may coexist. (shrink)

Mediating the meaning of evidence through epistemological diversity Nursing's disciplinary recognition of ‘multiple ways of knowing’ reflects an epistemological diversity that supports nursing praxis. Nursing as praxis offers a conceptual way to explore what it is about the interface of practice, knowledge and evidence in nursing that distinguishes us as a discipline. I suggest that the relationship between evidence and knowledge is defined and mediated by the same epistemological diversity that supports nursing as praxis. Just as the meaning and truth‐value (...) of evidence is evaluated from within the body of existing disciplinary knowledge, new evidence may prompt an evaluation of the meaning and truth‐value of extant nursing knowledge. Nursing practice that relies on scientific evidence as a singular basis of practice knowledge is susceptible to exploitation by the diverse agendas operating within an ideology of evidence‐based practice and the healthcare system. Mediating the meaning of evidence for nursing practice through acknowledgement of the diverse epistemologies that underpin nursing knowledge will contribute to a disciplinary‐specific definition of what constitutes evidence for nursing, and will better direct how evidence is integrated into a disciplinary body of knowledge. (shrink)

In a review of my book “Wahrscheinlichkeitslehre”, Dr. Ernest Nagel has recently criticized some of my ideas on probability and induction. His review includes a good exposition of my ideas, and I have to thank him for his serious attempts to do justice to my results. He attacks, however, some very essential points of my theory. I may be allowed, therefore, to answer him as frankly and thoroughly as he attacks me.

In pure science, the standard approach to non-epistemic values is to exclude them as far as possible from scientific deliberations. When science is applied to practical decisions, non-epistemic values cannot be excluded. Instead, they have to be combined with scientific information in a way that leads to practically optimal decisions. A normative model is proposed for the processing of information in both pure and applied science. A general-purpose corpus of scientific knowledge, with high entry requirements, has a central role in (...) this model. Due to its high entry requirements, the information that it contains is sufficiently reliable for the vast majority of practical purposes. However, for some purposes, the corpus needs to be supplemented with additional information, such as scientific indications of danger that do not satisfy the entry requirements for the corpus. The role of non-epistemic values in the evaluation of scientific information should, as far as possible, be limited to determining the level of evidence required for various types of practical decisions. (shrink)

Recent efforts to argue that nonepistemic values have a legitimate role to play in assessing scientific models, theories, and hypotheses typically either reject the distinction between epistemic and nonepistemic values or incorporate nonepistemic values only as a secondary consideration for resolving epistemic uncertainty. Given that scientific representations can legitimately be evaluated not only based on their fit with the world but also with respect to their fit with the needs of their users, we show in two case studies that nonepistemic (...) values can play a legitimate role as factors that override epistemic considerations in assessing scientific representations for practical purposes. (shrink)

Although epistemic values have become widely accepted as part of scientific reasoning, non-epistemic values have been largely relegated to the "external" parts of science (the selection of hypotheses, restrictions on methodologies, and the use of scientific technologies). I argue that because of inductive risk, or the risk of error, non-epistemic values are required in science wherever non-epistemic consequences of error should be considered. I use examples from dioxin studies to illustrate how non-epistemic consequences of error can and should be considered (...) in the internal stages of science: choice of methodology, characterization of data, and interpretation of results. (shrink)