Adaptability has emerged as an essential skill in the engineering workforce due to constant technological and social change, engineering grand challenges, and the recent global pandemic. Although engineering employers and national reports have called for increased adaptability among engineers, what adaptability means in the engineering workplace has not been investigated. This dissertation uses qualitative semi-structured critical incident interviews with engineering managers from four corporations to better understand their perceptions of adaptability and then incorporates these findings into a scenario-based intervention for the engineering classroom. Thematic analysis of the interviews with engineering managers expanded existing frameworks for workplace adaptability to provide an engineering-specific understanding of adaptability as a construct. Managers’ perceptions of adaptability span six dimensions, each important when teaching this competency to engineering students: Creative Problem Solving; Interpersonal Adaptability; Handling Work Stress; Dealing with Uncertain and Unpredictable Situations; Learning New Technologies, Tasks, and Procedures; and Cultural Adaptability. Managers’ beliefs about the importance of a balanced approach to being adaptable in different work contexts, and the influence of personal characteristics such as self-awareness and having had specific experiences related to being adaptable, emerged from the findings as well. Composite narratives reflecting real-life situations encountered by engineers in the workplace were developed based on findings from the engineering manager interviews to provide greater texture to the data. Six of the narratives mapped to the six dimensions of adaptability identified in the thematic analysis, while the seventh narrative illustrated the importance of balance and context when deciding whether and how to be adaptable. They revealed how multiple dimensions of adaptability work together and that contextual factors like support from managers and coworkers are integral to an engineer’s adaptability. The narratives were condensed into two scenarios for use in a classroom-based intervention with first-year engineering students at a large public university. After the intervention, many students’ definitions of adaptability became more multi-dimensional and reflective of adaptability context and balance. Students also reported a better understanding of engineering work, an expanded definition of adaptability, greater delineation of adaptability, increased self-awareness, greater appreciation for the importance of adaptability balance, and enhanced feelings of job preparedness.

Introduction: There are 350 to 400 pediatric heart transplants annually according to the Pediatric Heart Transplant Database (Dipchand et al. 2014). Finding appropriate donors can be challenging especially for the pediatric population. The current standard of care is a donor-to-recipient weight ratio. This ratio is not necessarily a parameter directly indicative of the size of a heart, potentially leading to ill-fitting allografts (Tang et al. 2010). In this paper, a regression model is presented - developed by correlating total cardiac volume to non-invasive imaging parameters and patient characteristics – for use in determining ideal allograft fit with respect to total cardiac volume.
Methods: A virtual, 3D library of clinically-defined normal hearts was compiled from reconstructed CT and MR scans. Non-invasive imaging parameters and patient characteristics were collected and subjected to backward elimination linear regression to define a model relating patient parameters to the total cardiac volume. This regression model was then used to retrospectively accept or reject an ‘ideal’ donor graft from the library for 3 patients that had undergone heart transplantation. Oversized and undersized grafts were also transplanted to qualitatively analyze virtual transplantation specificity.
Results: The backward elimination approach of the data for the 20 patients rejected the factors of BMI, BSA, sex and both end-systolic and end-diastolic left ventricular measurements from echocardiography. Height and weight were included in the linear regression model yielding an adjusted R-squared of 82.5%. Height and weight showed statistical significance with p-values of 0.005 and 0.02 respectively. The final equation for the linear regression model was TCV = -169.320+ 2.874h + 3.578w ± 73 (h=height, w=weight, TCV= total cardiac volume).
Discussion: With the current regression model, height and weight significantly correlate to total cardiac volume. This regression model and virtual normal heart library provide for the possibility of virtual transplant and size-matching for transplantation. The study and regression model is, however, limited due to a small sample size. Additionally, the lack of volumetric resolution from the MR datasets is a potentially limiting factor. Despite these limitations the virtual library has the potential to be a critical tool for clinical care that will continue to grow as normal hearts are added to the virtual library.