Ethical decision making has long been recognized as critical for industrial-organizational (I-O) psychologists in the variety of roles they fill in education, research, and practice. Decisions with ethical implications are not always readily apparent and often require consideration of competing concerns. The American Psychological Association (APA) Ethical Principles of Psychologists and Code of Conduct are the principles and standards to which all Society for Industrial and Organizational Psychology (SIOP) members are held accountable, and these principles serve to aid in decision making. To this end, the primary focus of this article is the presentation and application of an integrative ethical decision-making framework rooted in and inspired by empirical, philosophical, and practical considerations of professional ethics. The purpose of this framework is to provide a generalizable model that can be used to identify, evaluate, resolve, and engage in discourse about topics involving ethical issues. To demonstrate the efficacy of this general framework to contexts germane to I-O psychologists, we subsequently present and apply this framework to five scenarios, each involving an ethical situation relevant to academia, practice, or graduate education in I-O psychology. With this article, we hope to stimulate the refinement of this ethical decision-making model, illustrate its application in our profession, and, most importantly, advance conversations about ethical decision making in I-O psychology.

OBJECTIVES/SPECIFIC AIMS: The objective of this research was to assess the clinical impact of simulation-based team leadership training on team leadership effectiveness and patient care during actual trauma resuscitations. This translational work addresses an important gap in simulation research and medical education research. METHODS/STUDY POPULATION: Eligible trauma team leaders were randomized to the intervention (4-hour simulation-based leadership training) or control (standard training) condition. Subject-led actual trauma patient resuscitations were video recorded and coded for leadership behaviors (primary outcome) and patient care (secondary outcome) using novel leadership and trauma patient care metrics. Patient outcomes for trauma resuscitations were obtained through the Harborview Medical Center Trauma Registry and analyzed descriptively. A one-way ANCOVA analysis was conducted to test the effectiveness of our training intervention versus a control group for each outcome (leadership effectiveness and patient care) while accounting for pre-training performance, injury severity score, postgraduate training year, and days since training occurred. Association between leadership effectiveness and patient care was evaluated using random coefficient modeling. RESULTS/ANTICIPATED RESULTS: Sixty team leaders, 30 in each condition, completed the study. There was a significant difference in post-training leadership effectiveness [F(1,54)=30.19, p<.001, η2=.36] between the experimental and control conditions. There was no direct impact of training on patient care [F(1,54)=1.0, p=0.33, η2=.02]; however, leadership effectiveness mediated an indirect effect of training on patient care. Across all trauma resuscitations team leader effectiveness correlated with patient care (p<0.05) as predicted by team leadership conceptual models. DISCUSSION/SIGNIFICANCE OF IMPACT: This work represents a critical step in advancing translational simulation-based research (TSR). While there are several examples of high quality translational research programs, they primarily focus on procedural tasks and do not evaluate highly complex skills such as leadership. Complex skills present significant measurement challenges because individuals and processes are interrelated, with multiple components and emergent nature of tasks and related behaviors. We provide evidence that simulation-based training of a complex skill (team leadership behavior) transfers to a complex clinical setting (emergency department) with highly variable clinical tasks (trauma resuscitations). Our novel team leadership training significantly improved overall leadership performance and partially mediated the positive effect between leadership and patient care. This represents the first rigorous, randomized, controlled trial of a leadership or teamwork-focused training that systematically evaluates the impact on process (leadership) and performance (patient care).

Credibility and trustworthiness are the bedrock upon which any science is built. The strength of these foundations has been increasingly questioned across the sciences as instances of research misconduct and mounting concerns over the prevalence of detrimental research practices have been identified. Consequently, the purpose of this article is to encourage our scientific community to positively and proactively engage in efforts that foster a healthy and robust industrial and organizational (I-O) psychology. We begin by advancing six defining principles that we believe reflect the values of robust science and offer criteria for evaluating proposed efforts to change scientific practices. Recognizing that the contemporary scientific enterprise is a complex and diverse network of actors and institutions, we then conclude by identifying 12 stakeholders who play important roles in achieving a culture of robust science in I-O psychology and offer recommendations for actions we can take as members of these groups to strengthen our science.

The implicit philosophy for how research and practice in industrial and organizational (I-O) psychology has pursued inferences about our field's core phenomena has largely been based on a nomothetic, variable-based, and aggregate/“large-sample” ideal. As Tett, Hundley, and Christiansen (2017) expertly highlight, there are more insightful means for drawing inferences about the nature of such aggregate relationships based on meta-analytic techniques than the current practice in the organizational sciences. However, the motivating force behind our commentary has less to do with the issues raised by Tett et al. (2017) concerning the practice of using meta-analysis for purposes of validity generalization and more to do with the practice of using meta-analysis for purposes of scientific inference. Between-person philosophies in which the end-goal is to identify general conclusions that apply to the aggregate (cf. Hanges & Wang, 2012) have historically guided our scientific inferences and have supported the proliferation of meta-analytic techniques (including what Tett et al. describe as tertiary analyses based on such findings). These philosophies have led to a dearth of understanding at the within-person and social system levels—the levels at which most of our meaningful phenomena exist (e.g., Hamaker, 2012; Von Bertalanffy, 1950). Learning, performing, decision making, communicating, sense-making, feeling/expressing emotion: These are the concepts that drive the lived experiences of individuals both inside and outside of the workplace, and all are vulnerable to being misunderstood or misinterpreted by focusing only on aggregate evidence at the between-person level. Consequently, we wish to first supplement Tett et al.’s recommendations for drawing generalizability inferences in meta-analysis and suggest a “pre-emptive” question (i.e., Question 0) to the list of four they advance in their focal article.

Computed tomography (CT) has been commonly used in medicine for assessing the anatomy of humans in conventional computer axial tomography (CAT) scans. It is also a very common tool for assessing the architecture of trabecular bones for diagnosis of conditions such as osteoporosis. More recently, high-resolution CT (micro-CT) has found increasing use in materials science for the evaluation of the internal structure of a variety of advanced materials for industrial applications. Knowledge of the micro-architecture of these materials is extremely important to better understand their performance. Micro-CT is a non-destructive 3D characterization tool that uses X rays to determine the internal structure of objects through imaging of different densities within the scanned object. High-resolution laboratory-based micro-CT or nano-CT provides image resolution on the order of 300 nm. Such high resolution allows one to visualize the internal 3D structure of fine-scale features. The data from micro-CT results in a virtual rendering of the object under investigation, which allows one to travel through the volume in any direction and angle, revealing complex hidden structures within the object. Thus, micro-CT can be an important complementary technique for a microscopy laboratory.