Teaching students to teach patients: A theory-guided approach

Nurses in every setting provide patient teaching on a routine basis, often several times a day. Patient teaching skills are essential competencies to be developed during pre-licensure nursing education. While students learn what to teach for specific conditions, they often lack competence in how to teach in ways that individualize and optimize patient learning. The ultimate goal of patient teaching is to arm patients with the knowledge and skills, and the desire and confidence in their ability to reach their targeted health outcomes. We describe the creation of a theoretical framework to guide development of patient teaching skills. The framework, rooted in the contemporary health care values of patient-centered care, is a synthesis of four evidence-based approaches to patient teaching: patient engagement, motivational interviewing, adult learning theory, and teach-back method. Specific patient teaching skills, derived from each of the approaches, are applied within the context of discharge teaching, an important nursing practice linked to patient outcomes. This exemplar emphasizes the use of critical teaching process skills and targeted informational content. An online student learning module based on the theoretical framework and combined with simulation experiences provides the nurse educator with one strategy for use with nursing students. The theoretical framework has applicability for skill development during pre-licensure education and skill refinement for nurses in clinical practice

Validation of highly reliable, real-time knowledge-based systems

Knowledge-based systems have the potential to greatly increase the capabilities of future aircraft and spacecraft and to significantly reduce support manpower needed for the space station and other space missions. However, a credible validation methodology must be developed before knowledge-based systems can be used for life- or mission-critical applications. Experience with conventional software has shown that the use of good software engineering techniques and static analysis tools can greatly reduce the time needed for testing and simulation of a system. Since exhaustive testing is infeasible, reliability must be built into the software during the design and implementation phases. Unfortunately, many of the software engineering techniques and tools used for conventional software are of little use in the development of knowledge-based systems. Therefore, research at Langley is focused on developing a set of guidelines, methods, and prototype validation tools for building highly reliable, knowledge-based systems. The use of a comprehensive methodology for building highly reliable, knowledge-based systems should significantly decrease the time needed for testing and simulation. A proven record of delivering reliable systems at the beginning of the highly visible testing and simulation phases is crucial to the acceptance of knowledge-based systems in critical applications

Similarity and the trustworthiness of distributive judgements

When people must either save a greater number of people from a smaller harm or a smaller number from a greater harm, do their choices reflect a reasonable moral outlook? We pursue this question with the help of an experiment. In our experiment, two-fifths of subjects employ a similarity heuristic. When alternatives appear dissimilar in terms of the number saved but similar in terms of the magnitude of harm prevented, this heuristic mandates saving the greater number. In our experiment, this leads to choices that are inconsistent with all standard theories of justice. We argue that this demonstrates the untrustworthiness of distributive judgments in cases that elicit similarity-based choice

Optimal management of urinary tract infections in older people

Urinary tract infections (UTI) occur frequently in older people. Unfortunately, UTI is commonly overdiagnosed and overtreated on the basis of nonspecific clinical signs and symptoms. The diagnosis of a UTI in the older patient requires the presence of new urinary symptoms, with or without systemic symptoms. Urinalysis is commonly used to diagnose infection in this population, however, the evidence for its use is limited. There is overwhelming evidence that asymptomatic bacteriuria should not be treated. Catheter associated urinary tract infection accounts for a significant amount of hospital-associated infection. Indwelling urinary catheters should be avoided where possible and alternatives sought. The use of narrow spectrum antimicrobial agents for urinary tract infection is advocated. Local guidelines are now widely used to reflect local resistance patterns and available agents. Guidelines need to be updated to reflect changes in antimicrobial prescribing and a move from broad to narrow spectrum antimicrobials

Driving and sustaining culture change in Olympic sport performance teams: A first exploration and grounded theory

Stimulated by growing interest in the organizational and performance leadership components of Olympic success, sport psychology researchers have identified Performance Director-led culture change as a process of particular theoretical and applied significance. To build on initial work in this area, and develop practically meaningful understanding, a pragmatic research philosophy and grounded theory methodology were engaged to uncover culture change best practice from the perspective of newly appointed Performance Directors. Delivered in complex and contested settings, results revealed that the optimal change process consisted of an initial evaluation, planning, and impact phase adjoined to the immediate and enduring management of a multi-directional, perception- and power-based social system. As the first inquiry of its kind, these findings provide a foundation for the continued theoretical development of culture change in Olympic sport performance teams and a first model on which applied practice can be based

Toward a Strategic Human Resource Management Model of High Reliability Organization Performance

In this article, we extend strategic human resource management (SHRM) thinking to theory and research on high reliability organizations (HROs) using a behavioral approach. After considering the viability of reliability as an organizational performance indicator, we identify a set of eight reliability-oriented employee behaviors (ROEBs) likely to foster organizational reliability and suggest that they are especially valuable to reliability seeking organizations that operate under “trying conditions”. We then develop a reliability-enhancing human resource strategy (REHRS) likely to facilitate the manifestation of these ROEBs. We conclude that the behavioral approach offers SHRM scholars an opportunity to explain how people contribute to specific organizational goals in specific contexts and, in turn, to identify human resource strategies that extend the general high performance human resource strategy (HPHRS) in new and important ways

The Intuitive Appeal of Explainable Machines

Algorithmic decision-making has become synonymous with inexplicable decision-making, but what makes algorithms so difficult to explain? This Article examines what sets machine learning apart from other ways of developing rules for decision-making and the problem these properties pose for explanation. We show that machine learning models can be both inscrutable and nonintuitive and that these are related, but distinct, properties. Calls for explanation have treated these problems as one and the same, but disentangling the two reveals that they demand very different responses. Dealing with inscrutability requires providing a sensible description of the rules; addressing nonintuitiveness requires providing a satisfying explanation for why the rules are what they are. Existing laws like the Fair Credit Reporting Act (FCRA), the Equal Credit Opportunity Act (ECOA), and the General Data Protection Regulation (GDPR), as well as techniques within machine learning, are focused almost entirely on the problem of inscrutability. While such techniques could allow a machine learning system to comply with existing law, doing so may not help if the goal is to assess whether the basis for decision-making is normatively defensible. In most cases, intuition serves as the unacknowledged bridge between a descriptive account and a normative evaluation. But because machine learning is often valued for its ability to uncover statistical relationships that defy intuition, relying on intuition is not a satisfying approach. This Article thus argues for other mechanisms for normative evaluation. To know why the rules are what they are, one must seek explanations of the process behind a model’s development, not just explanations of the model itself