Reflections at the finish line: The activities, roles, and relationships of successful first-generation college students

Nearly ninety percent of first-generation college students (FGCS) in the United States fail to graduate within six years of enrollment in postsecondary education (Saenz, Hurtado, Barrera, Wolf, & Yeung, 2007). Empirical investigation into the lived experiences of FGCS is necessary to enhance appreciation of undergraduate student retention and to inform practices designed to encourage crossing the finish line to degree completion. The study examines undergraduate retention using qualitative data collected through student interviews with successful FGCS at a large, public research university in the southern United States. Guided by a theoretical lens, informed significantly by bioecological systems theory, the activities, roles, and relationships of successful FGCS are described. Relations among activities, roles, relationships, and positive college outcomes (successful transition, retention, and graduation) are explored. This study contributes to what is known about FGCS experiences. This contribution is important for the development of programs and supports to encourage four-year degree attainment for FGCS. The study also responds to an omission in the literature on FGCS experiences. Although literature has examined the struggles of first-generation and low-income students, studies have not examined the experiences of successful students. For the purposes of this study, a successful student is a student who has been retained and is within one semester of four-year undergraduate degree completion. This study describes the experiences of successful students utilizing the voices of students themselves. It describes what persistence looks like and feels like through the eyes of students from historically under resourced and underperforming groups. Finally, this study responds to the need for a new paradigm for appreciating undergraduate retention. As such, this study utilizes a developmental perspective to study the experiences of successful students. Study findings include four thematic areas describing the undergraduate experience of successful first generation college students; a) mentoring, b) developing academic competencies, c) engagement and exploration, and d) developing goals and autonomy. Recommendations support the development and implementation of proactive retention and degree-completion strategies from a strengths-based perspective

The Activities, Roles, and Relationships of Successful First-Generation College Students

This qualitative study describes the experiences of 16 successful first-generation college students (FGCS) utilizing a theoretical lens, informed significantly by bioecological systems theory (Bronfenbrenner, 1979), which guided our qualitative analyses of interview transcripts to examine the activities, roles, and relationships of these students as they approached the finish line of college graduation. We responded to an omission in the literature on FGCS experiences: although researchers have examined the struggles of first-generation and low-income students, few studies have focused on the experiences of successful students. We offer a developmental paradigm for appreciating undergraduate retention. Recommendations from this investigation support the development and implementation of proactive retention and degree-completion strategies from a strengths-based perspective

Some Advice for Psychologists Who Want to Work With Computer Scientists on Big Data

This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous opportunities for personnel assessment practice in handling and interpreting this kind of data. We argue that psychologists and computer scientists can benefit from interdisciplinary collaboration. This article aims to inform psychologists who are interested in working with computer scientists about the potentials of interdisciplinary collaboration, as well as the challenges such as differing terminologies, foci of interest, data quality standards, approaches to data analyses, and diverging publication practices. Finally, we provide recommendations preparing psychologists who want to engage in collaborations with computer scientists. We argue that psychologists should proactively approach computer scientists, learn computer scientific fundamentals, appreciate that research interests are likely to converge, and prepare novice psychologists for a data-oriented scientific future

Some Advice for Psychologists Who Want to Work with Computer Scientists on Big Data

This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous opportunities for personnel assessment practice in handling and interpreting this kind of data. We argue that psychologists and computer scientists can benefit from interdiscip

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Some Advice for Psychologists Who Want to Work With Computer Scientists on Big Data

This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous opportunities for personnel assessment practice in handling and interpreting this kind of data. We argue that psychologists and computer scientists can benefit from interdisciplinary collaboration. This article aims to inform psychologists who are interested in working with computer scientists about the potentials of interdisciplinary collaboration, as well as the challenges such as differing terminologies, foci of interest, data quality standards, approaches to data analyses, and diverging publication practices. Finally, we provide recommendations preparing psychologists who want to engage in collaborations with computer scientists. We argue that psychologists should proactively approach computer scientists, learn computer scientific fundamentals, appreciate that research interests are likely to converge, and prepare novice psychologists for a data-oriented scientific future.Multimedia Computin