Preparation of Female and Minority PhD and Post-Docs for Careers in Engineering Academia (Experience)

Improving undergraduate STEM teaching for diverse students is dependent to some extent on increasing the representation of Black, Indigenous and People of Color (BIPOC) and women in the ranks of faculty in engineering departments. However, new faculty members, whether they had postdoctoral training or not, report that they were not adequately prepared for academia. To address this need, a professional development program was developed for underrepresented doctoral and postdoctoral students, which focused on various strategies to be successful in teaching, research and service aspects of academic positions. The program included an intensive two-week summer session, with follow-up mentoring during the academic year, and was conducted from 2017 to 2020 with three cohorts of fellows recruited from across the country. To evaluate the impact of the program on the participants' perceptions of their preparation for academic careers, a follow up survey was sent in May 2021 to the three former cohorts of participants (n=61), and responses were received from 37 of them. The survey asked participants to reflect on areas that they felt most prepared for in their academic positions, and areas that they felt least prepared for. The survey also asked participants to discuss additional supports they would have liked to have been provided with to better prepare them given their current positions (academic, industry, etc.). Results from the survey indicated that 92% of participants found the professional development program prepared them for the responsibilities and expectations to succeed in academic positions. Over 90% agreed that the program prepared them for the application process for a tenure track search, and 89% agreed the program prepared them for the primary components of the startup package. In addition, participants reported that the program increased their preparation in developing teaching philosophy (100%), developing learning outcomes (97%), and using active learning strategies during teaching (91%). The majority agreed that the program helped prepare them to teach students with various cultural backgrounds, and to develop and use assessment strategies. Participants were also asked to discuss the impact of the Covid 19 pandemic on their career trajectory, and most of them reported being somewhat impacted (65%) to extremely impacted (29%). Participants reported few or no job openings, cancelations of interviews, delays in research which impacted the rate of completing degrees, and publications, which affected the participants' application competitiveness. Furthermore, working from home and balancing family and academic responsibilities affected their productivity. Based on the survey results, funds were secured to provide an additional day of professional training to cover any items not addressed during summer training, as well as any issues, challenges, or concerns they might have encountered while fulfilling their academic position. Thirty-three ACADEME fellows have indicated that they will participate in the new professional development, held in May 2022. Results from this analysis, and preliminary topics and outcomes of the supplemental activities are discussed. The findings contribute to the literature by increasing knowledge of specific challenges that new faculty encounter and can inform future efforts to support minorities and women in engineering doctoral programs. © American Society for Engineering Education, 2022.

Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: Development, application and comparison of machine learning and deep learning methods.

BACKGROUND: COVID-19 infected millions of people and increased mortality worldwide. Patients with suspected COVID-19 utilised emergency medical services (EMS) and attended emergency departments, resulting in increased pressures and waiting times. Rapid and accurate decision-making is required to identify patients at high-risk of clinical deterioration following COVID-19 infection, whilst also avoiding unnecessary hospital admissions. Our study aimed to develop artificial intelligence models to predict adverse outcomes in suspected COVID-19 patients attended by EMS clinicians. METHOD: Linked ambulance service data were obtained for 7,549 adult patients with suspected COVID-19 infection attended by EMS clinicians in the Yorkshire and Humber region (England) from 18-03-2020 to 29-06-2020. We used support vector machines (SVM), extreme gradient boosting, artificial neural network (ANN) models, ensemble learning methods and logistic regression to predict the primary outcome (death or need for organ support within 30 days). Models were compared with two baselines: the decision made by EMS clinicians to convey patients to hospital, and the PRIEST clinical severity score. RESULTS: Of the 7,549 patients attended by EMS clinicians, 1,330 (17.6%) experienced the primary outcome. Machine Learning methods showed slight improvements in sensitivity over baseline results. Further improvements were obtained using stacking ensemble methods, the best geometric mean (GM) results were obtained using SVM and ANN as base learners when maximising sensitivity and specificity. CONCLUSIONS: These methods could potentially reduce the numbers of patients conveyed to hospital without a concomitant increase in adverse outcomes. Further work is required to test the models externally and develop an automated system for use in clinical settings.

GLOBAL OUTCOMES OF THE POSITIVE PSYCHIATRY AND MENTAL HEALTH MASSIVE OPEN ONLINE COURSE (MOOC): A COMPARISON OF COHORTS BEFORE AND DURING THE PANDEMIC

Background: In 2017 with a University of Sydney grant we designed, developed and delivered a positive psychiatry and Mental Health Massive Open Online Course (MOOC), offering a 5-week course to fill a gap in global mental health (MH) education. We included a research arm to evaluate the MOOC, which has since delivered online education to consumers, carers, clinicians and the interested public internationally with more than 185,000 registered learners, over 95,000 active learners, and over 10,000 course completers by mid-October 2021. We noted a large upswing in users with the onset of the COVID-19 pandemic and the platform host recommended our MOOC as a pandemic resource. Objectives: To examine the impact of the pandemic on participants and outcomes by comparing cohorts before and during the pandemic. Methods: We compared the quantitative outcomes between groups at baseline and after substantial course completion (n = 4734), before (n = 2340) and during (n = 2394) the pandemic. We examined online measures of mental health (MH) knowledge (an in-house test), the Depression, Anxiety and Stress Scale-21 items (DASS-21), the Flourishing Scale and stigma. Findings: The MOOC demonstrated significant improvements across measures (p < 0.05) in both cohorts, and demonstrably larger improvements in flourishing (p < 0.01) and stigma (p < 0.05) in the pandemic cohort. Conclusion: A MOOC can provide significant improvements in MH, knowledge, flourishing and stigma in non-pandemic and pandemic participants.

Prognostic accuracy of triage tools for adults with suspected COVID-19 in a prehospital setting: An observational cohort study

Aims/Objectives/Background Emergency Medical Service (EMS) and other practitioners assessing patients with suspected COVID-19 in the community must rapidly determine whether patients need treatment in hospital or can self-care. Tools to triage patient acuity have only been validated in hospital populations. We aimed to estimate the accuracy of five risk-stratification tools recommended to predict severe illness and compare accuracy to existing clinical decision-making in a pre-hospital setting. Methods/Design An observational cohort study using linked ambulance service data for patients assessed by EMS crews in the Yorkshire and Humber region of England between 18th March 2020 and 29th June 2020 was conducted to assess performance of the PRIEST tool, NEWS2, the WHO algorithm, CRB-65 and PMEWS in patients with suspected COVID-19 infection. The primary outcome was death or need for organ support. Results/Conclusions Of 7550 patients in our cohort, 17.6% (95% CI:16.8% to 18.5%) experienced the primary outcome. The NEWS2, PMEWS, PRIEST tool and WHO algorithm identified patients at risk of adverse outcomes with a high sensitivity (>0.95) and specificity ranging between 0.3 (NEWS2) and 0.41 (PRIEST tool). The high sensitivity of NEWS2 and PMEWS was achieved by using lower thresholds than previously recommended (NEWS2;0-1 vs 2+ and PMEWS;0-2 vs 3+). On index (first) assessment, 65% of patients were transported to hospital and EMS decision to transfer patients achieved a sensitivity of 0.84 (95% CI 0.83 to 0.85) and specificity of 0.39 (95% CI 0.39 to 0.40) to the primary outcome. This does not account for clinical reasons not to convey patients to hospital who subsequently deteriorated. Use of NEWS2, PMEWS, PRIEST tool and WHO algorithm could therefore potentially improve EMS triage of patients with suspected COVID-19 infection. Use of the PRIEST tool could significantly increase the sensitivity of triage without increasing the number of patients conveyed to hospital. (Table Presented).

Simulation Agent-Based Model to Demonstrate the Transmission of COVID-19 and Effectiveness of Different Public Health Strategies

COVID-19 has changed the world fundamentally since its outbreak in January 2020. Public health experts and administrations around the world suggested and implemented various intervention strategies to slow down the transmission of the virus. To illustrate to the general public how the virus is transmitted and how different intervention strategies can check the transmission, we built an agent-based model (ABM) to simulate the transmission of the virus in the real world and demonstrate how to prevent its spread with public health strategies. © Copyright © 2021 Wang, Xiong, Liu, Jung, Stone and Chukoskie.

Nurses' health beliefs about paper face masks in Japan, Australia and China: a qualitative descriptive study.

AIM: To explore the health beliefs of clinical and academic nurses from Japan, Australia and China regarding wearing paper masks to protect themselves and others, and to identify differences in participants' health beliefs regarding masks. BACKGROUND: The correct use of face masks and consensus among health professionals across the globe is essential for containing pandemics, and nurses need to act according to policy to protect themselves, educate the public and preserve resources for frontline health workers. Paper masks are worn by health professionals and the general public to avoid the transmission of respiratory infections, such as COVID-19, but there appear to be differences in health beliefs of nurses within and between countries regarding these. METHODS: This qualitative descriptive study used content analysis with a framework approach. FINDINGS: There were major differences in nurse participants' beliefs between and within countries, including how nurses use paper masks and their understanding of their efficacy. In addition, there were cultural differences in the way that nurses use masks in their daily lives and nursing practice contexts. CONCLUSION: Nurses from different working environments, countries and areas of practice hold a variety of health beliefs about mask wearing at the personal and professional level. IMPLICATIONS FOR NURSING POLICY AND HEALTH POLICY: The COVID-19 pandemic has sparked much discussion about the critical importance of masks for the safety of health professionals, and there has been considerable discussion and disagreement about health policies regarding mask use by the general public. Improper use of masks may have a role in creating mask shortages or transmitting infections. An evidence-based global policy on mask use for respiratory illnesses for health professionals, including nurses, and the general public needs to be adopted and supported by a wide-reaching education campaign.