Recognition-primed Decision Model Research Articles

Nursing students' decision-making regarding postpartum hemorrhage: An exploration using the recognition-primed decision model

AimTo explore the knowledge content and structure of nursing students' decision-making in a high-stake clinical situation of postpartum hemorrhage using the Recognition-Primed Decision Model. BackgroundAccording to research on clinical judgment, a nurse’s expectations for a patient situation are central to the clinical decision-making process. However, little research has addressed the expectation concept and its relationship with the nurse’s knowledge. Grounded in the naturalistic decision-making paradigm, the Recognition-Primed Decision Model provides a potential framework to describe the content and structure of nurses’ knowledge and expectations as they unfold in high-stake clinical situations, such as postpartum hemorrhage. As it is typically used in studies of expert decision-making, it is crucial to test the adequacy of the Model with a student population and refine the research methods for using this framework. DesignDescriptive design where qualitative data were analyzed using qualitative and quantitative methods. MethodsA convenience sample of 53 students enrolled in a maternal and child health course in the Fall of 2021 was formed. As part of an online exercise to prepare for a simulation, they read a vignette presenting the story of a woman experiencing postpartum hemorrhage and recorded their answers to questions designed to probe their decision-making. Recordings were transcribed and subjected to content analysis based on the four components of recognition according to the Recognition-Primed Decision Model (i.e., cues, expectations, goals and actions). FindingsAll participants recognized the postpartum hemorrhage. Their knowledge was organized into clusters representing the potential causes (i.e., tone, trauma, tissue and thrombin) and consequences (i.e., hemodynamic instability) of postpartum hemorrhage, as well as other potential issues (e.g., pain and comfort, baby and partner, infection). Although students could identify relevant cues and actions, they had difficulties articulating their longer-term goals and expectations for the mother and care outcomes. ConclusionsThis study showed the potential of the Recognition-Primed Decision Model to organize the content and structure of the knowledge that supported nursing students' decision-making in a high-stake situation. The findings suggest that their knowledge disproportionately focuses on the cause-and-effect relations between cues and actions. They invite further consideration of longer-term goals and expectations in nursing education to prepare students to anticipate events and assess patient responses appropriately.

Naturalistic Decision-Making in Sport: How Current Advances Into Recognition Primed Decision Model Offer Insights for Future Research in Sport Settings?

Naturalistic Decision-Making in Sport: How Current Advances Into Recognition Primed Decision Model Offer Insights for Future Research in Sport Settings?

Pilot decision‐making during a dual engine failure on take‐off: Insights from three different decision‐making models

AbstractCommercial airline pilots are required to make efficient, justifiable, and safety‐critical decisions when faced with adverse events such as engine failures. Although these are rare events, the consequences are severe, and the pilot response is critical. This paper reviews pilot decision‐making when faced with a dual engine failure on take‐off using three different decision models; the Recognition Primed Decision Model, Decision Ladders and the Perceptual Cycle Model. In‐depth interviews with eight experienced airline pilots were conducted to capture their decision‐making processes in response to a dual engine failure on take‐off event. The analysis of these interviews using the three different decision models provide recommendations for a proposed decision assistant. The different decision models are discussed in relation to the insight they can bring to developing a future decision assistant tool within the flight deck of commercial aircraft.

Motivating healthcare professionals (nurses, nurse assistants, physicians) to integrate new practices for preventing healthcare-associated infections into the care continuum: turning Positive Deviance into positive norms

BackgroundThe literature examining healthcare-associated infections (HAI) points to two main problems in conforming to infection prevention and control (IPC) guidelines among healthcare professionals (HP). One is the discrepancy between HPs’ behavioral intentions and their implementation in practice. The other refers to how HPs maintain these practices after the intervention stage ends. The method proposed in this study seeks to address both these issues by using the Positive Deviance (PD) approach to focus on the dissemination stage of interventions. The study seeks to offer a method for disseminating 27 PD practices to 135 HPs, among them nurses, nurse assistants and physicians, so as to help them maintain IPC guidelines, offer feedback on the dissemination process and examine the impact of the dissemination stage on changes in their behavior.MethodsThe theoretical model underlying this qualitative research was the Recognition-Primed Decision (RPD) model, which we implemented in the field of healthcare-associated infections (HAIs). Moreover, we used the Discovery & Action Dialogue (DAD) and Think Aloud (TA) techniques to describe the methodological development of simulations for HPs. Feedback from the HP demonstrators underwent content analysis, while descriptive statistics were used to characterize behavioral changes.ResultsHPs’ information processing regarding infection prevention shifts from peripheral/automatic processing to intuition and analytical/central processing, turning PD practices into positive norms. The HPs personally experienced finding a solution and made repeated corrections until they overcame the barriers. Most of the HPs (69.4%) reported that the practices were fully implemented, together with additional practices.ConclusionsImplementation of the dissemination stage indicates that in order for HPs to integrate and assimilate practices that are not in the official guidelines, merely observing simulations is not sufficient. Rather, each staff member must personally carry out the procedures.

Nurse Cognition, Decision Support, and Barcode Medication Administration: A Conceptual Framework for Research, Practice, and Education.

This article synthesizes theoretical perspectives related to nurse cognition. We present a conceptual model that can be used by multiple stakeholders to study and contemplate how nurses use clinical decision support systems, and specifically, Barcode-Assisted Medication Administration, to make decisions during the delivery of care. Theoretical perspectives integrated into the model include dual process theory, the Cognitive Continuum Theory, human factors engineering, and the Recognition-Primed Decision model. The resulting framework illustrates the process of nurse cognition during Barcode-Assisted Medication Administration. Additionally, the model includes individual or human and environmental factors that may influence nurse cognition and decision making. It is important to consider the influence of individual, human, and environmental factors on the process of nurse cognition and decision making. Specifically, it is necessary to explore the impact of heuristics and biases on clinician decision making, particularly related to the development of alarm and alert fatigue. Aided by the proposed framework, stakeholders may begin to identify heuristics and cognitive biases that influence the decision of clinicians to accept or override a clinical decision support system alert and whether heuristics and biases are associated with inappropriate alert override.

MACRO-COGNITIVE ANALYSIS OF DESIGN SKILLS FOR SUPPORTING ENGINEERING DESIGN EDUCATION

Macro-cognition is a field of study that investigates how domain experts (e.g., firefighters and weather forecasters) make decisions and judgements for their work. This paper has adapted two models from macro-cognition to better understand design activities. The first one is the Data / Frame model, and it tells how a person’s frame can influence the selection of information in the design process. The second one is the recognition-primed decision (RPD) model, which shows how a design idea emerges with a person’s recognition and mental simulation. Based on these models, we further suggest three intervention strategies (i.e., challenge, relate and demonstrate) to support the processes of sensemaking, recognition and mental simulation. We also suggest two evaluation aspects, i.e., relevance and fluency, to assess design activities.

Multi-Agent Simulation of Individuals’ Escape in the Urban Rainstorm Context Based on Dynamic Recognition-Primed Decision Model

The urban rainstorm can evolve into a serious emergency, generally characterized by high complexity, uncertainty, and time pressure. It is often difficult for individuals to find the optimal response strategy due to limited information and time constraints. Therefore, the classical decision-making method based on the “infinite rationality” assumption is sometimes challenging to reflect the reality. Based on the recognition-primed decision (RPD) model, a dynamic RPD (D-RPD) model is proposed in this paper. The D-RPD model assumes that decision-makers can gain experience in the escaping process, and the risk perception of rainstorm disasters can be regarded as a Markov process. The experience of recent attempts would contribute more in decision-making. We design the agent according to the D-RPD model, and employ a multi-agent system (MAS) to simulate individuals’ decisions in the context of a rainstorm. Our results show that experience helps individuals to perform better when they escape in the rainstorm. Recency acts as a one of the key elements in escaping decision making. We also find that filling the information gap between individuals and real-time disaster would help individuals to perform well, especially when individuals tend to avoid extreme decisions.

A perception survey on the roles of nurses during triage in a selected public hospital in Kwazulu-Natal Province, South Africa.

Introductiontriage is gradually becoming an autonomous nursing role essential to patients' safety and the efficient delivery of emergency care. The increased need for more holistic and advanced care during triage makes the role of nurses during triage highly indispensable. However, several studies have shown that nurse-led triage has been so successful over the years in most African countries and in other developing countries. South African Triage Scale (SATS) is an example of triage tool that was designed in such a way that the lowest cadre nurse can successfully implement. The success recorded by this tool made most African countries and some other developing countries adopt the tool. The study was designed to explore the roles of nurses during triage in a selected public hospital in KwaZulu-Natal province.Methodsthis study utilized a quantitative approach, in which a non-experimental survey involving convenience sampling technique was chosen as the most suitable sampling technique for the study. Recognition-primed decision model formed the framework of the study. Ethical clearance was obtained from University of KwaZulu-Natal Ethics Review Board and ethics principles were observed during the study.Resultsthe result of the study indicated that majority (100%) of the respondents perceived that nurses have lots of roles to perform during triage. They further unveiled that it is highly paramount for nurses to manage the waiting room and control overcrowding in the unit.Conclusionthe study draws on the need for qualified and experienced nurses to be in charge of these roles in order to reduce the mortality and morbidity rates that usually occur during triage administration.

Handball Goalkeeper Intuitive Decision-Making: A Naturalistic Case Study.

Goalkeepers hold a key position for success in team sports competitions. They perform in dynamical contexts and are highly submitted to time pressure. The purpose of this naturalistic case study, therefore, was to explore how a handball expert goalkeeper deals with the uncertainty of the competition settings to make successful decisions. An individual self-confrontation interview was held with a goalkeeper while he watched duels with potential throwers in an official competition. A mixed method was used combining the first-person and third-person point of view. Verbal data were supplemented by observational data (distance measures between the goalkeeper and the potential thrower) in 83 short accounts of decision-making situations. Qualitative analysis resulted in 419 units of salient features, in three types of processes related to the Recognition-Primed Decision model, and in four micro-decisions. Non-parametrical statistical analysis indicated that there was a significant effect of distances between the potential thrower and the goalkeeper, on the micro-decision categories, but not on the recognition processes. These results provide insights into cognitive contents and processes an expert goalkeeper can use under uncertainty and time pressure. The mixed method furnishes a meaningful description and a subsequent understanding of expert performances in sport.

On the realization of the recognition-primed decision model for artificial agents

This work proposes a methodology to program an artificial agent that can make decisions based on a naturalistic decision-making approach called recognition-primed decision model (RPDM). The proposed methodology represents the main constructs of RPDM in the language of Belief-Desire-Intention logic. RPDM considers decision-making as a synthesis of three phenomenal abilities of the human mind. The first is one’s use of experience to recognize a situation and suggest appropriate responses. The main concern here is on situation awareness because the decision-maker needs to establish that a current situation is the same or similar to one previously experienced, and the same solution is likely to work this time too. To this end, the proposed modeling approach uses a Markov logic network to develop an Experiential-Learning and Decision-Support module. The second component of RPDM deals with the cases when a decision-maker’s experience becomes secondary because the situation has not been recognized as typical. In this case, RPDM suggests a diagnostic mechanism that involves feature-matching, and, therefore, an ontology (of the domain of interest) based reasoning approach is proposed here to deal with all such cases. The third component of RPDM is the proposal that human beings use intuition and imagination (mental stimulation) to make sure whether a course of action should work in a given situation or not. Mental simulation is modeled here as a Bayesian network that computes the probability of occurrence of an effect when a cause is more likely. The agent-based model of RPDM has been validated with real (empirical) data to compare the simulated and empirical results and develop a correspondence in terms of the value of the result, as well as the reasoning.

Augmented Reality Adaptive Training Principles

As augmented reality (AR) technology offers enhanced capabilities for medical simulation training, research is required to understand how to best leverage the strengths of AR. We describe a literature review that will provide the foundation for articulating and testing candidate principles for AR adaptive training design. Our goal is to articulate and test design principles for designing recognition skills training as described in the recognition-primed decision model (Klein, 1997). We identified seminal articles from the the Naturalistic Decision Making literature, and used those to seed a systematic review of the medical education and combat medic training literatures. Findings from the literature review suggest three categories of candidate design principles: 1) fidelity and realism, 2) engagement, and 3) scaffolding. Next steps will include articulating specific design principles and designing a series of studies to test them using the VPIT AR training platform.

Deriving the Practice-Primed Decision Model from a naturalistic decision-making perspective for acute care nursing research

AimThe purpose of this article is to present the derivation of the Practice Primed Decision Model from a naturalistic decision-making framework for use in guiding future nursing decision-making research. BackgroundAcute care nurses make decisions in demanding environments under the influence of many factors. The influence of these factors on nurse decision-making is not well understood leading to gaps in understanding how to best support acute care nurse decision-making. MethodsThe strategy of theory derivation was used in the development of a new model for use in nursing research. This model incorporates important elements identified in naturalistic decision making, a Recognition Primed Decision Model and an integrative review of nurse decision-making literature. ConclusionThe new model, Practice Primed Decision Model, provides a new perspective to guide nurse decision-making research. This model includes factors influential to the nurse decision-making process that is more realistic in time limited, high stakes decision-making situations.

Scaffolding the Decision-making Concept through Scenarios in isiZulu Folk Narratives for Life-Skills Education

 This article discusses the decision-making concept as simplified through scenarios in three isiZulu folk narratives. The three folk narratives are part of the qualitative data that were generated from 63 student participants who studied isiZulu folklore education at a South African University. During the data gathering process, each participant told one folk narrative of their choice that was narrated to them earlier in their lives, they stated who the narrator was, the rationale for narrating the story, knowledge, and skill(s) learned from the story and they stated how the story influenced their lives. The findings revealed that isiZulu folk narratives are used to simplify life skills concepts such as decision-making, which might be difficult for young people to understand and implement. Using the Naturalistic Decision-making (NDM) theory that is entwined with the Recognition-Primed Decision (RPD) model, I argue that the folk narratives provide young people with knowledge about possible life situations and this sensitises them to their immediate environment, and sharpens their decision-making and survival skills. The decision-making concept is also part of the life skills education in the school curriculum of the South African schools. Therefore, this article presents a case for the appropriateness of an African folklore approach to be used in schools to scaffold difficult concepts in the pedagogy of life skills-based curriculum.Â

A Recognition Primed Decision Model for Public Consumption of Government Data

The U.S. Census Bureau (Census) collects and maintains data on the nation’s population, neighborhoods, and economy, and shares that information with the public digitally. To successfully provide value to the public, data platforms and the data itself must be constructed in a way that individuals can easily use and understand. However, like many data providers, Census introduces technical terminology that can complicate the user’s experience. This paper describes how such terms affect individuals’ ability to consume data, by applying a recognition primed decision making method. The data used come from 36 in-depth interviews and 1 focus group with data consumers. An empirical model of the recognition-primed decision process uncovered in the data is presented, also elucidating key variations in decision paths according to factors like technical training or experience. These findings illustrate that failing to present user-centered data can limit the ability of consumers to benefit from public information.

The General’s Intuition

Can we trust the operational intuitions of generals? The proponents of the overconfidence model, one of the most influential perspectives in the psychology of judgment, commonly offer a skeptical answer. Generals’ operational intuitions are likely to be hampered by overconfidence and negatively affect military effectiveness. However, the successful operational outcome of General Douglas MacArthur’s decision to land at Inchon (June–September 1950) seemingly contradicts the model. We seek to complement and refine the overconfidence model by examining the Inchon landing decision through the analytical lens of the recognition-primed decision model. This model typically envisions that under specific circumstances—notably an experienced decision maker, an adequate environment regularity, and an opportunity to learn—generals are able to make quick and satisfactory decisions. We show that such a configuration was present in the Inchon landing case and helps explain both the decision process and its successful outcome.

Predicting pilot behavior during midair encounters using recognition primed decision model

Next Generation Air Transportation System is designed to deal with the dramatic increase of air traffic. To achieve this goal, new technologies and operation concepts are to be introduced. In this paper, a novel pilot behavior model is developed to support design and analysis of new technologies and operation concepts that are to be used in midair encounter scenarios. In this method, Bayesian network is used to build a probabilistic framework for human and automation components. To model human pilot, different human information processing stages which include perception, decision making and response are formulated. Especially, this paper firstly proposes a recognition primed decision model to describe human pilot’s decision making process. In addition, a structural pilot model is introduced to promote the accuracy of existing pilot behavior models. Finally, this paper shows how this method could be used to study midair encounter scenarios. Meanwhile, the validity and feasibility of this method are investigated and discussed.

Intuitive Cognition

In the naturalistic decision-making literature, intuitive cognition is at the heart of a pattern recognition–based decision model called the recognition-primed decision model. Given the importance of intuitive cognition in naturalistic decision-making theory, we explore the question of what makes intuitive cognition effective for decision making and, in so doing, present an extended empirical and theoretical foundation for the intuitive component in naturalistic decision making. We theorize that intuitive cognition is effective because it (1) possesses a capability for grounded, situational meaning making (sign interpretation); (2) is operative over extended work intervals involving interruptions; and (3) is instrumental in handling situated complexities of everyday living. Other characteristics of intuitive cognition and its foundations are discussed. We propose that intuitive cognition represents the core of cognition—grounded, situational meaning making—whereas analytical cognition represents a form of an intellectual exoskeleton that provides added capabilities (e.g., working memory).

Situation Assessment and Decision Making Strategies of Emergency Medical Services Physicians in Routine and Non-Routine Situations

Naturalistic decision making (NDM) research is focused on macrocognition - cognitive functions and processes used by experienced people working in real-world settings (Crandall, Klein, & Hoffman, 2006). These settings characterize ill-structured problems; uncertain dynamic environments; shifting, ill-defined, or competing goals; action/feedback loops; time stress; high stakes; multiple players; and organizational goals and norms (Orasanu & Connoly, 1993). Exactly such setting, with multifactorial influences on cognition, is health care. In this field evidence based medicine (EBM; Rosenberg & Donald, 1995) is preferred, representing the normative approach, sometimes difficult to meet in real-world situations. By contrast, the main idea of NDM is that professionals rely heavily on their previous experience stored in memory, as described by the Recognition-Primed Decision model (RPD model; Klein, 1997). It is viewed as the experiential basis of intuition that enables rapid recognition of situations and decisions. Simply put, intuition is recognition (Simon, 1992).Two of the primary macrocognitive functions are situation assessment and decision making (Klein et al., 2003), included in the RPD model. Situation assessment (SA) is about understanding of what is going on in the course of dynamic unfolding situations, during which elaboration of an existing SA or shifting to a new SA may occur repeatedly (Kaempf, Wolf, & Miller, 1993). From the SA viewpoint, clear situations are usually immediately identified (recognized), and ambiguous situations need to be diagnosed. In NDM, the term diagnosing has not been used only in a medical context, it has been used to express deliberative SA regarding different situational problems as well. Based on the review (Zsambok, Beach, & Klein, 1992), there are the following SA strategies included in the RPD model: pattern matching - is about matching larger observed patterns with remembered patterns of previous situations; feature matching - is about matching observed features with remembered features of previous situations; and mental simulation - is about imagining the likely causal chain of events that preceded the observed situation. Adopting the theory of two processes (Evans, 2007), pattern matching, as an intuitive process, is experiencebased, fast and cognitively effortless and mental simulation, as a deliberative process, is slower and more cognitively demanding (see Klein, 1999). Feature matching is a dominant SA strategy (Kaempf et al., 1993) that can be viewed as an intuitive process, when a decision maker immediately recognizes the features, and as a deliberative process, when a decision maker consciously notices the features and uses them to retrieve a hypothesis or to contrast different hypotheses, concerning the particular unclear situation (Zsambok et al., 1992).SA is usually followed by decision making (DM), which is defined as the selection of one option from a set of options (Klein, Calderwood, & Clinton-Cirocco, 2010) and is interleaved with acting in complex realistic situations (Orasanu & Conolly, 1993), requiring more than one decision. One way to make a decision is a straightforward identification of the situation along with an identification of the typical single option, which is usually automatically implemented. This experience-based, intuitive DM strategy, which corresponds with the RPD model, prevails in experienced people (Klein, 1999). However, when it is not clear what action to take, more cognitively demanding strategies are necessary. There are distinguished two types of option evaluation (Klein et al., 2010; Klein, Calderwood, & MacGregor, 1989): sequential evaluation, when only one option is examined at a time, and concurrent evaluation, when several options are considered at the same time. The first one is characteristic for mental simulation, as the way of imagining the likely consequences of a single option, arriving at a satisficing decision. …

A reference decision model of first responders' decision-making

This paper introduces a generic decision model that connects three basic components in the response decision process: the stochastic information flow consisting of help requests and resource availabilities; situation awareness based on a spatiotemporal risk picture and response decisions allocating resources for response actions. The formalism utilised is the stochastic marked point process. The paper is written to support 'black-box' modelling of situation awareness in virtual training simulation, where a trainee's performance is compared with, e.g., the performance of experienced commanders with respect to key performance indicators (KPIs) obtained from such simulations. The paper also briefly discusses the use of the introduced decision model in developing agent-based models and simulations. It is also noted that the decision model encompasses the recognition-primed decision (RPD) model of naturalistic decision-making.

A reference decision model of first responders' decision-making

This paper introduces a generic decision model that connects three basic components in the response decision process: the stochastic information flow consisting of help requests and resource availabilities; situation awareness based on a spatiotemporal risk picture and response decisions allocating resources for response actions. The formalism utilised is the stochastic marked point process. The paper is written to support 'black-box' modelling of situation awareness in virtual training simulation, where a trainee's performance is compared with, e.g., the performance of experienced commanders with respect to key performance indicators (KPIs) obtained from such simulations. The paper also briefly discusses the use of the introduced decision model in developing agent-based models and simulations. It is also noted that the decision model encompasses the recognition-primed decision (RPD) model of naturalistic decision-making.