Capitalizing on strengths and minimizing weaknesses of veterans in civilian employment interviews: Perceptions of interviewers and veteran interviewees.

Like all job applicants, veterans have to face the ubiquitous employment interview and pass this potential hurdle to civilian sector employment. So, because of the uniqueness of transitioning from the military to civilian employment, the present paper sought to identify perceived interviewing strengths and weaknesses of veteran interviewees from (a) the perspective of civilian sector human resource professionals (i.e. hiring personnel) with experience interviewing veterans (Study 1, five focus groups, N = 14), and (b) veterans (Study 2, N = 93). Qualitative analysis of the focus group transcripts resulted in the emergence of two theme categories: (1) veteran interviewee strengths and (2) veteran interviewee weaknesses. This information guided the development of a 10-item survey that was completed by 93 veterans (Study 2). In its totality, the results (from both Study 1 and Study 2) indicated that communication of soft skills, confidence, and professionalism were perceived to be strengths that veterans displayed during civilian employment interviews, and conversely, the ineffective translation and communication of relevant technical skills acquired in the military, use of military jargon, and nervousness were considered to be weaknesses. Recommendations to capitalize on the strengths and mitigate the weaknesses are presented.

Location-based collective distress using large-scale biosignals in real life for walkable built environments.

Biosignals from wearable sensors have shown great potential for capturing environmental distress that pedestrians experience from negative stimuli (e.g., abandoned houses, poorly maintained sidewalks, graffiti, and so forth). This physiological monitoring approach in an ambulatory setting can mitigate the subjectivity and reliability concerns of traditional self-reported surveys and field audits. However, to date, most prior work has been conducted in a controlled setting and there has been little investigation into utilizing biosignals captured in real-life settings. This research examines the usability of biosignals (electrodermal activity, gait patterns, and heart rate) acquired from real-life settings to capture the environmental distress experienced by pedestrians. We collected and analyzed geocoded biosignals and self-reported stimuli information in real-life settings. Data was analyzed using spatial methods with statistical and machine learning models. Results show that the machine learning algorithm predicted location-based collective distress of pedestrians with 80% accuracy, showing statistical associations between biosignals and the self-reported stimuli. This method is expected to advance our ability to sense and react to not only built environmental issues but also urban dynamics and emergent events, which together will open valuable new opportunities to integrate human biological and physiological data streams into future built environments and/or walkability assessment applications.