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This study aims to describe the self-perceived risk of housing loss among low-income individuals in the context of four potential life shocks and examines whether individuals’ social and economic resources are protective against self-perceived risk of housing loss in the context of these life shocks.

The authors conducted a national online survey of low-income households in the USA. The authors asked respondents to assess whether they would be at risk of housing loss if they experienced any of four potential life shocks: job loss, moderate and major medical catastrophes and damage to home from natural disaster. Logistic regression models estimated the association between self-perceived risk of housing loss in the context of these life shocks and respondents’ economic and social resources, adjusting for sociodemographic covariates.

A minority of respondents reported that they would be able to maintain their housing if they were to experience a life shock (i.e. 34%–43% depending on the specific life shock). Receipt of more generous forms of income support as well as having family or friends to live with in the event of housing loss were the most consistent economic and social resource predictors of respondents’ self-perceived ability to maintain their housing in the context of a life shock.

The findings suggest that comprehensive assessments of risk of housing crises should take into account how individuals view their vulnerability to housing loss if they were to experience a life shock.

In this study, students’ perceptions of the effective practices of their research mentors were examined. The research mentors implemented the practices informed by the Center for the Improvement of Mentored Experiences in Research (CIMER) mentorship competencies to mentor underrepresented students in engineering education research in a virtual environment.

This research experience for undergraduates (REU) program, situated in the United States of America, consisted of undergraduate students (i.e. mentees), graduate students and faculty mentors who all had at least one underrepresented identity in engineering (i.e. Black, Latiné/x, and/or women). Using qualitative methods, we used data from reflection surveys and follow-up interviews with REU mentees to understand the outcomes of the mentorship strategies employed by the mentors in the program. The data were analyzed thematically using CIMER model constructs and social capital theory as guiding frameworks.

The results indicated the identified strategies students perceived as the most impactful for mentorship throughout the program. Students in the REU gained knowledge on how to activate social capital in mentorship relationships and how to better mentor others.

The findings provide insight on how to operationalize the CIMER mentorship competencies to skillfully mentor underrepresented students in engineering. Given the size of the REU and the nature of qualitative research, the sample size was limited.

The results help inform mentorship practices for underrepresented individuals in engineering education and the workforce. Further, they add to the practical knowledge of implementing CIMER best practices virtually, at a time when the world has transitioned to more hybrid and virtual working and learning environments.

This study identifies impactful strategies for operationalizing mentorship strategies informed by theory- and evidence-based CIMER mentorship competencies. In addition, this study extends knowledge about how to implement mentoring best practices and engage mentorship in a virtual environment.

This study aims to analyze the performance of quality indices to continuously validate a predictive model focused on the control chart classification.

The research method used analytical statistical methods to propose a classification model. The project science research concepts were integrated with the statistical process monitoring (SPM) concepts using the modeling methods applied in the data science (DS) area. For the integration development, SPM Phases I and II were associated, generating models with a structured data analysis process, creating a continuous validation approach.

Validation was performed by simulation and analytical techniques applied to the Cohen’s Kappa index, supported by voluntary comparisons in the Matthews correlation coefficient (MCC) and the Youden index, generating prescriptive criteria for the classification. Kappa-based control charts performed well for m = 5 sample amounts and n = 500 sizes when Pe is less than 0.8. The simulations also showed that Kappa control requires fewer samples than the other indices studied.

The main contributions of this study to both theory and practitioners is summarized as follows: (1) it proposes DS and SPM integration; (2) it develops a tool for continuous predictive classification models validation; (3) it compares different indices for model quality, indicating their advantages and disadvantages; (4) it defines sampling criteria and procedure for SPM application considering the technique’s Phases I and II and (5) the validated approach serves as a basis for various analyses, enabling an objective comparison among all alternative designs.