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There has been an alarming increase in the prevalence of mental health concerns amongst undergraduate students. Engineering students experiencing mental health distress are less likely to seek professional help than are non-engineering students. Lack of treatment can result in the escalation of mental health symptoms among engineering students. This study, supported by an NSF Research Initiation in Engineering Formation grant, focused on characterizing engineering students’ beliefs about seeking help for a mental health concern. Using the integrated behavioral model as a framework, 33 semi-structured qualitative interviews were conducted with engineering students from a wide range of majors, years of study, and social identity groups. Interviews were analyzed through deductive coding to identify key beliefs associated with help-seeking as defined by the integrated behavioral model. The beliefs identified include a desire among engineering students to fix their own problems, to avoid admitting imperfection, and fear of being seen by others when seeking help for a mental health concern. These results were used to create an engineering mental health help-seeking instrument containing items related to perceived outcomes/attributes, experiential (i.e., affective) beliefs, barriers/facilitators, and perceived norms associated with help seeking. This instrument is currently being refined through cognitive interviews, and pilot data will be collected to examine evidence of instrument reliability and validity. The finalized instrument will be used to identify those beliefs that are predictive of help-seeking intention and behavior. These beliefs are prime targets for future interventions designed to increase mental health help-seeking in the undergraduate engineering student population.
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Board 236: Design for Sustainability: How Mental Models of Social-Ecological Systems Shape Engineering Design Decisions
Understanding how engineers connect technical work to broader social-ecological systems is critical because their designs transform societies and environments. As part of a national study to explore how civil and chemical engineers navigate design decisions, we are developing a survey instrument to assess mental models of social-ecological-technical systems (SETS). Mental models (Johnson-Laird, 2001; Rouse & Morris, 1986), are internal representations that individuals use to describe, explain, and predict the form, function, state, and purpose of a system. In this case, the system is the connection between technical design and broader social-ecological systems. The project is informed by three frameworks: 1) planned behavior, 2) mental models, and 3) social-ecological-technical systems (SETS). The project integrates the theory of planned behavior with mental models to build fundamental knowledge of engineers’ mental models of SETSs, changes in their mental models over time, and relationships between mental models and design decisions. This paper presents the instrument development process centered on eliciting mental models of SETS. SETS (McPhearson et al., 2022) is a generalized framework that positions social, technical, and ecological elements of a system as vertices of a triangle, with interactions in all directions. The instrument will include both closed-ended and open-ended items, allowing us to leverage advances in natural language processing to scale qualitative data analysis and combine an inferential framework often associated with quantitative studies with the richer information flow associated with qualitative studies. Previous work using SETS has identified individual components within each vertex salient to the specific context (Bixler et al., 2019). In this paper, we report on the phases of instrument development that support this contextualization: 1) Initial interview protocol development followed by semi-structured interviews with six engineering students outside the target majors to test how well the protocol elicits information about students mental models of SETS, 2) revisions to the interview protocol followed by semi-structured interviews with senior-level students in chemical and civil engineering students (12 per discipline), 3) deductive and inductive analysis of those interviews, using SETS as our deductive coding scheme followed by inductive coding to refine and contextualize the analysis and support survey development. We conclude with the initial survey instrument, which will undergo pilot testing in the summer of 2024. The results both support instrument development and offer an exploratory analysis of civil and chemical engineering students’ mental models of SETS.
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- Award ID(s):
- 2300977
- PAR ID:
- 10545847
- Publisher / Repository:
- American Society for Engineering Education
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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